diff --git a/docs/guide/dataframes.asciidoc b/docs/guide/dataframes.asciidoc index ddd58a3..616a31d 100644 --- a/docs/guide/dataframes.asciidoc +++ b/docs/guide/dataframes.asciidoc @@ -11,7 +11,7 @@ without overloading your machine. ------------------------------------- >>> import eland as ed >>> # Connect to 'flights' index via localhost Elasticsearch node ->>> df = ed.DataFrame('localhost:9200', 'flights') +>>> df = ed.DataFrame('http://localhost:9200', 'flights') # eland.DataFrame instance has the same API as pandas.DataFrame # except all data is in Elasticsearch. See .info() memory usage. diff --git a/docs/guide/machine-learning.asciidoc b/docs/guide/machine-learning.asciidoc index 317d3d8..d5858f5 100644 --- a/docs/guide/machine-learning.asciidoc +++ b/docs/guide/machine-learning.asciidoc @@ -19,7 +19,7 @@ model in Elasticsearch # Import the model into Elasticsearch >>> es_model = MLModel.import_model( - es_client="localhost:9200", + es_client="http://localhost:9200", model_id="xgb-classifier", model=xgb_model, feature_names=["f0", "f1", "f2", "f3", "f4"], diff --git a/docs/guide/overview.asciidoc b/docs/guide/overview.asciidoc index d569cab..a4990a1 100644 --- a/docs/guide/overview.asciidoc +++ b/docs/guide/overview.asciidoc @@ -20,13 +20,13 @@ The recommended way to set your requirements in your `setup.py` or [discrete] === Getting Started -Create a `DataFrame` object connected to an {es} cluster running on `localhost:9200`: +Create a `DataFrame` object connected to an {es} cluster running on `http://localhost:9200`: [source,python] ------------------------------------ >>> import eland as ed >>> df = ed.DataFrame( -... es_client="localhost:9200", +... es_client="http://localhost:9200", ... es_index_pattern="flights", ... ) >>> df diff --git a/docs/sphinx/examples/demo_notebook.ipynb b/docs/sphinx/examples/demo_notebook.ipynb index 14731cd..a3313ee 100644 --- a/docs/sphinx/examples/demo_notebook.ipynb +++ b/docs/sphinx/examples/demo_notebook.ipynb @@ -64,7 +64,7 @@ }, "outputs": [], "source": [ - "ed_flights = ed.DataFrame('localhost', 'flights')" + "ed_flights = ed.DataFrame('http://localhost:9200', 'flights')" ] }, { @@ -3927,7 +3927,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -3960,7 +3960,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] diff --git a/docs/sphinx/examples/online_retail_analysis.ipynb b/docs/sphinx/examples/online_retail_analysis.ipynb index 31982c4..a17175b 100644 --- a/docs/sphinx/examples/online_retail_analysis.ipynb +++ b/docs/sphinx/examples/online_retail_analysis.ipynb @@ -53,7 +53,7 @@ "outputs": [], "source": [ "df = ed.csv_to_eland(\"data/online-retail.csv.gz\",\n", - " es_client='localhost', \n", + " es_client='http://localhost:9200', \n", " es_dest_index='online-retail', \n", " es_if_exists='replace', \n", " es_dropna=True,\n", @@ -1218,7 +1218,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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S57t6AFBVlaTmokFVtQHYALBq1aoaHx+fi8N2NjExwWJpy1zre2yX3/H0tPW2nDO+/xszh/p+zvoamySpv2bSp3orsLWq7mrrNzNIsh9v3Tpov59o27cBxw7tf0wrm6pckiRJGmnTJtVV9RjwaJLXtqJTgPuBW4HJETzWAre05VuBc9soICcDT7VuIp8DTk1yeHtA8dRWJkmSJI20mXT/AHgvcH2SQ4CHgPMZJOQ3JbkAeAQ4q9W9DTgD2Aw80+pSVTuSfBC4u9X7QFXtmJMoJEmSpAU0o6S6qr4GrNrLplP2UreAC6c4zrXAtbNonyRJkrToOaOiJEmS1JFJtSRJktSRSbUkiSRLktyc5JtJHkjyliRHJNmU5MH2+/BWN0muSrI5ydeTnDB0nLWt/oNJ1k79ipLULybVkiSAK4HPVtXrgDcCDzCYk+D2qloB3M7zcxScDqxoP+uAqwGSHAFcApwEnAhcMpmIS1LfmVRL0gEuyWHAzwLXAFTV31bVk8AaYGOrthE4sy2vAa6rgTuBJW2+gtOATVW1o6p2ApuA1fMWiCQtoJkOqSdJ6q/lwHeAP0zyRuAe4H3AWJtnAOAxYKwtLwUeHdp/ayubqvwnJFnH4C43Y2Njs56afuxlsH7l7hesM6rT3e/atWtk2z4TfY6vz7FB/+PryqRaknQwg5ly31tVdyW5kue7egCD4VKT1Fy9YFVtADYArFq1qmY7Nf1Hr7+Fy+994Y+wLefM7piLxcTEBLP99xglfY6vz7FB/+Pryu4fkqStwNaququt38wgyX68deug/X6ibd8GHDu0/zGtbKpySeo9k2pJOsBV1WPAo0le24pOAe4HbgUmR/BYC9zSlm8Fzm2jgJwMPNW6iXwOODXJ4e0BxVNbmST1nt0/JEkA7wWuT3II8BBwPoMbLzcluQB4BDir1b0NOAPYDDzT6lJVO5J8ELi71ftAVe2YvxAkaeGYVEuSqKqvAav2sumUvdQt4MIpjnMtcO2cNk6SRoDdPyRJkqSOTKolSZKkjkyqJUmSpI5MqiVJkqSOTKolSZKkjmaUVCfZkuTeJF9L8uVWdkSSTUkebL8Pb+VJclWSzUm+nuSEoeOsbfUfTLJ2qteTJEmSRsls7lT/k6p6U1VNDrl0EXB7Va0Abuf5KW1PB1a0n3XA1TBIwoFLgJOAE4FLJhNxSZIkaZR16f6xBtjYljcCZw6VX1cDdwJL2vS2pwGbqmpHVe0ENgGrO7y+JEmStCjMdPKXAj6fpID/UFUbgLE2LS3AY8BYW14KPDq079ZWNlX5j0myjsEdbsbGxpiYmJhhE/evXbt2LZq2zLW+x7Z+5XPT1hu1+Pt+zvoamySpv2aaVP/jqtqW5O8Am5J8c3hjVVVLuDtrCfsGgFWrVtX4+PhcHLaziYkJFktb5lrfY7v8jqenrbflnPH935g51Pdz1tfYJEn9NaPuH1W1rf1+Avg0gz7Rj7duHbTfT7Tq24Bjh3Y/ppVNVS5JkiSNtGmT6iSHJnnl5DJwKvAN4FZgcgSPtcAtbflW4Nw2CsjJwFOtm8jngFOTHN4eUDy1lUmSJEkjbSbdP8aATyeZrP+fquqzSe4GbkpyAfAIcFarfxtwBrAZeAY4H6CqdiT5IHB3q/eBqtoxZ5FIkiRJC2TapLqqHgLeuJfy7wGn7KW8gAunONa1wLWzb6YkSZK0eDmjoiRJktSRSbUkSZLUkUm1JEmS1JFJtSRJktSRSbUkSZLUkUm1JEmS1JFJtSRJktSRSbUkSZLUkUm1JEmS1JFJtSRJktSRSbUkSZLUkUm1JEmS1JFJtSRJktSRSbUkSZLUkUm1JEmS1NGMk+okByX5apI/a+vLk9yVZHOSG5Mc0spf0tY3t+3Lho5xcSv/VpLT5jwaSZIkaQHM5k71+4AHhtY/DFxRVccBO4ELWvkFwM5WfkWrR5LjgbOB1wOrgY8lOahb8yVJkqSFN6OkOskxwDuAP2jrAd4G3NyqbATObMtr2jpt+ymt/hrghqp6tqoeBjYDJ85BDJIkSdKCOniG9X4X+HXglW39SODJqtrd1rcCS9vyUuBRgKraneSpVn8pcOfQMYf3+ZEk64B1AGNjY0xMTMywifvXrl27Fk1b5lrfY1u/8rlp641a/H0/Z32NTZLUX9Mm1UneCTxRVfckGd/fDaqqDcAGgFWrVtX4+H5/yRmZmJhgsbRlrvU9tsvveHraelvOGd//jZlDfT9nfY1NktRfM7lT/Vbg55OcAbwUeBVwJbAkycHtbvUxwLZWfxtwLLA1ycHAYcD3hsonDe8jSZIkjaxp+1RX1cVVdUxVLWPwoOEXquoc4IvAu1q1tcAtbfnWtk7b/oWqqlZ+dhsdZDmwAvjSnEUiSZIkLZAu41T/BvCrSTYz6DN9TSu/Bjiylf8qcBFAVd0H3ATcD3wWuLCqpu/sKkmaFw6dKkn7bqYPKgJQVRPARFt+iL2M3lFVPwDePcX+lwKXzraRkqR5MTl06qva+uTQqTck+fcMhky9mqGhU5Oc3er98z2GTn0N8J+T/H1voEg6EDijoiTJoVMlqaNZ3amWJPXW7zJPQ6dC9+FTx14G61fufsE6ozo0Y9+HlexzfH2ODfofX1cm1ZJ0gJvvoVOh+/CpH73+Fi6/94U/wkZtqMxJfR9Wss/x9Tk26H98XZlUS5IcOlWSOrJPtSQd4Bw6VZK68061JGkqvwHckORDwFf58aFTP9GGTt3BIBGnqu5LMjl06m4cOlXSAcSkWpL0Iw6dKkn7xu4fkiRJUkcm1ZIkSVJHJtWSJElSRybVkiRJUkcm1ZIkSVJHjv4hSeqlZRd9Zto6Wy57xzy0RNKBwDvVkiRJUkcm1ZIkSVJHJtWSJElSR9Mm1UlemuRLSf4yyX1JfruVL09yV5LNSW5Mckgrf0lb39y2Lxs61sWt/FtJTttvUUmSJEnzaCZ3qp8F3lZVbwTeBKxOcjLwYeCKqjoO2Alc0OpfAOxs5Ve0eiQ5HjgbeD2wGvhYkoPmMBZJkiRpQUybVNfArrb64vZTwNuAm1v5RuDMtrymrdO2n5IkrfyGqnq2qh4GNgMnzkUQkiRJ0kKa0ZB67Y7yPcBxwO8DfwU8WVW7W5WtwNK2vBR4FKCqdid5Cjiyld85dNjhfYZfax2wDmBsbIyJiYnZRbSf7Nq1a9G0Za71Pbb1K5+btt6oxd/3c9bX2CRJ/TWjpLqqngPelGQJ8GngdfurQVW1AdgAsGrVqhofH99fLzUrExMTLJa2zLW+x3b5HU9PW2/LOeP7vzFzqO/nrK+xSZL6a1ajf1TVk8AXgbcAS5JMJuXHANva8jbgWIC2/TDge8Ple9lHkiRJGlkzGf3jp9odapK8DPg54AEGyfW7WrW1wC1t+da2Ttv+haqqVn52Gx1kObAC+NIcxSFJkiQtmJl0/zga2Nj6Vb8IuKmq/izJ/cANST4EfBW4ptW/BvhEks3ADgYjflBV9yW5Cbgf2A1c2LqVSJIkSSNt2qS6qr4OvHkv5Q+xl9E7quoHwLunONalwKWzb6YkSZK0eDmjoiRJktSRSbUkSZLUkUm1JEmS1JFJtSRJktSRSbUkSZLUkUm1JEmS1NGMpimX+m7ZRZ+Zts6Wy94xDy2RJEmjyDvVkiRJUkcm1ZIkSVJHJtWSJElSRybVkiRJUkcm1ZIkSVJHJtWSJElSRybVkiRJUkcm1ZIkSVJH0ybVSY5N8sUk9ye5L8n7WvkRSTYlebD9PryVJ8lVSTYn+XqSE4aOtbbVfzDJ2v0XliRJkjR/ZnKnejewvqqOB04GLkxyPHARcHtVrQBub+sApwMr2s864GoYJOHAJcBJwInAJZOJuCRJkjTKpk2qq2p7VX2lLf8N8ACwFFgDbGzVNgJntuU1wHU1cCewJMnRwGnApqraUVU7gU3A6rkMRpIkSVoIs+pTnWQZ8GbgLmCsqra3TY8BY215KfDo0G5bW9lU5ZIkSdJIO3imFZO8AvgT4P1V9f0kP9pWVZWk5qJBSdYx6DbC2NgYExMTc3HYznbt2rVo2jLX+h7b+pXPzcmxFtO/Ud/PWV9jW6ySHAtcx+DmSAEbqurK1m3vRmAZsAU4q6p2ZvABcCVwBvAMcN7kN5rteZl/0w79oaraiCQdAGaUVCd5MYOE+vqq+lQrfjzJ0VW1vXXveKKVbwOOHdr9mFa2DRjfo3xiz9eqqg3ABoBVq1bV+Pj4nlUWxMTEBIulLXOt77FdfsfTc3KsLeeMz8lx5kLfz1lfY1vEJp+d+UqSVwL3JNkEnMfg2ZnLklzE4NmZ3+DHn505icGzMycNPTuzikFyfk+SW1uXP0nqtZmM/hHgGuCBqvp3Q5tuBSZH8FgL3DJUfm4bBeRk4KnWTeRzwKlJDm8PKJ7ayiRJC8hnZySpu5ncqX4r8C+Be5N8rZX9JnAZcFOSC4BHgLPattsYfCW4mcHXgucDVNWOJB8E7m71PlBVO+YiCEnS3JivZ2e6dvUbexmsX7l7VvvszWLsatT3LlB9jq/PsUH/4+tq2qS6qu4AMsXmU/ZSv4ALpzjWtcC1s2mgJGl+zNezM+14nbr6ffT6W7j83hk/FjSlxdSta1Lfu0D1Ob4+xwb9j68rZ1SUJL3gszNt+0yfndlbuST1nkm1JB3gfHZGkrrr/t2ZJGnU+eyMJHVkUi1JBzifnZGk7uz+IUmSJHVkUi1JkiR1ZFItSZIkdWRSLUmSJHVkUi1JkiR15Ogf0hxZdtFnpq2z5bJ3zENLJEnSfPNOtSRJktSRSbUkSZLUkUm1JEmS1JFJtSRJktSRSbUkSZLUkUm1JEmS1NG0SXWSa5M8keQbQ2VHJNmU5MH2+/BWniRXJdmc5OtJThjaZ22r/2CStfsnHEmSJGn+zeRO9ceB1XuUXQTcXlUrgNvbOsDpwIr2sw64GgZJOHAJcBJwInDJZCIuSZIkjbppk+qq+gtgxx7Fa4CNbXkjcOZQ+XU1cCewJMnRwGnApqraUVU7gU38ZKIuSZIkjaR9nVFxrKq2t+XHgLG2vBR4dKje1lY2VflPSLKOwV1uxsbGmJiY2Mcmzq1du3YtmrbMtb7Htn7lc3NyrOn+jdav3N35GDPV93PW19i0+Ew3E6qzoEqaqc7TlFdVJam5aEw73gZgA8CqVatqfHx8rg7dycTEBIulLXOt77FdfsfTc3KsLeeMv+D282YyTfk0x5ipvp+zvsYmSeqvfR394/HWrYP2+4lWvg04dqjeMa1sqnJJkiRp5O1rUn0rMDmCx1rglqHyc9soICcDT7VuIp8DTk1yeHtA8dRWJkmSJI28abt/JPkkMA4clWQrg1E8LgNuSnIB8AhwVqt+G3AGsBl4BjgfoKp2JPkgcHer94Gq2vPhR2lRm67vpSRJOnBNm1RX1S9MsemUvdQt4MIpjnMtcO2sWidJkiSNAGdUlCRJkjoyqZYkSZI6MqmWJEmSOjKpliRJkjrqPPmLpMXJmeIkSZo/3qmWJEmSOjKpliRJkjqy+4ckSVOYyaRPdqWSBN6pliRJkjozqZYkSZI6MqmWJEmSOjKpliRJkjoyqZYkSZI6MqmWJEmSOjKpliRJkjpynGpJveGYwpKkhTLvSXWS1cCVwEHAH1TVZfPdBvXHdEnU+pW7WUz/d5yrpO/ebU9x3gyONR9MZLWnA+19fibXwEx4nUijbV6zjSQHAb8P/BywFbg7ya1Vdf98tkNazGbyAb1+5Tw0hLlLFnTg8H1e0oFqvm/hnQhsrqqHAJLcAKwBfLM9wJisaU+TfxPrV+6e8i68d/JGgu/z+2gm18BMTHed+O2StH+kqubvxZJ3Aaur6pfa+r8ETqqq9wzVWQesa6uvBb41bw18YUcB313oRuwnxjZ6+hoXLM7YfrqqfmqhGzEKZvI+38q7vtcvxr+TudLn2KDf8fU5Nuh/fK+tqlfu686Lp7NpU1UbgA0L3Y49JflyVa1a6HbsD8Y2evoaF/Q7Nj2v63t9n/9O+hwb9Du+PscGB0Z8Xfaf7yH1tgHHDq0f08okSf3g+7ykA9J8J9V3AyuSLE9yCHA2cOs8t0GStP/4Pi/pgDSv3T+qaneS9wCfYzDU0rVVdd98tqGDRdclZQ4Z2+jpa1zQ79h6bx7f5/v8d9Ln2KDf8fU5NjC+FzSvDypKkiRJfeQ05ZIkSVJHJtWSJElSRybVM5RkfZJKclRbT5KrkmxO8vUkJyx0G2cryb9N8s3W/k8nWTK07eIW27eSnLaAzdwnSVa3tm9OctFCt6eLJMcm+WKS+5Pcl+R9rfyIJJuSPNh+H77Qbd0XSQ5K8tUkf9bWlye5q527G9vDbhLQr2sb+n99Q7+v8SRLktzcPksfSPKWvpy7JL/S/ia/keSTSV46yucuybVJnkjyjaGyvZ6rfc3xTKpnIMmxwKnAt4eKTwdWtJ91wNUL0LSuNgFvqKp/APx/wMUASY5n8MT+64HVwMcymHp4JOT5aZJPB44HfqHFNKp2A+ur6njgZODCFs9FwO1VtQK4va2PovcBDwytfxi4oqqOA3YCFyxIq7To9PDahv5f39Dva/xK4LNV9TrgjQziHPlzl2Qp8MvAqqp6A4OHjs9mtM/dxxnkNMOmOlf7lOOZVM/MFcCvA8NPda4BrquBO4ElSY5ekNbto6r6fFXtbqt3MhhPFgax3VBVz1bVw8BmBlMPj4ofTZNcVX8LTE6TPJKqantVfaUt/w2DN+2lDGLa2KptBM5ckAZ2kOQY4B3AH7T1AG8Dbm5VRjIu7Te9urah39c39PsaT3IY8LPANQBV9bdV9SQ9OXcMRoh7WZKDgZcD2xnhc1dVfwHs2KN4qnO1TzmeSfU0kqwBtlXVX+6xaSnw6ND61lY2qv5X4M/b8qjHNurtn1KSZcCbgbuAsara3jY9BowtVLs6+F0G/2H9H239SODJof/s9ebcaU709tqGXl7f0O9rfDnwHeAPW/eWP0hyKD04d1W1DfgIg2/otwNPAffQn3M3aapztU/vNSbVQJL/3PoM7fmzBvhN4P9c6Dbuq2lim6zzWwy+grx+4Vqq6SR5BfAnwPur6vvD22owNuZIjY+Z5J3AE1V1z0K3RVpofbu+4YC4xg8GTgCurqo3A0+zR1ePET53hzO4W7sceA1wKD/ZdaJX5uJczevkL4tVVb19b+VJVjL4g/rLwTdWHAN8JcmJjMhUvFPFNinJecA7gVPq+UHLRyK2FzDq7f8JSV7M4AP3+qr6VCt+PMnRVbW9fS31xMK1cJ+8Ffj5JGcALwVexaB/4pIkB7e7ISN/7jSnendtQ2+vb+j/Nb4V2FpVd7X1mxkk1X04d28HHq6q7wAk+RSD89mXczdpqnO1T+813ql+AVV1b1X9napaVlXLGFxAJ1TVYwym3T23PSF6MvDU0FcIIyHJagZfy/18VT0ztOlW4OwkL0mynEFH/S8tRBv3Ua+mSW59EK8BHqiqfze06VZgbVteC9wy323roqourqpj2rV1NvCFqjoH+CLwrlZt5OLSftWraxv6e31D/6/xlgs8muS1regU4H56cO4YdPs4OcnL29/oZGy9OHdDpjpX+5TjOaPiLCTZwuBJ2O+2P7LfY/B1yDPA+VX15YVs32wl2Qy8BPheK7qzqv5V2/ZbDPpZ72bwdeSf7/0oi1O7M/K7PD9N8qUL26J9l+QfA/8FuJfn+yX+JoN+lzcBfxd4BDirqvZ8CGMkJBkH/nVVvTPJzzB4AO0I4KvAL1bVswvYPC0ifbq24cC4vqG/13iSNzF4CPMQ4CHgfAY3LEf+3CX5beCfM8gDvgr8EoN+xSN57pJ8EhgHjgIeBy4B/pS9nKt9zfFMqiVJkqSO7P4hSZIkdWRSLUmSJHVkUi1JkiR1ZFItSZIkdWRSLUmSJHVkUi1JkiR1ZFItSZIkdfT/A04laE5zMHRJAAAAAElFTkSuQmCC", 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" ] @@ -1251,7 +1251,7 @@ "outputs": [ { "data": { - "image/png": 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" ] diff --git a/eland/common.py b/eland/common.py index 6ca3e08..93a283a 100644 --- a/eland/common.py +++ b/eland/common.py @@ -309,8 +309,10 @@ def elasticsearch_date_to_pandas_date( def ensure_es_client( es_client: Union[str, List[str], Tuple[str, ...], Elasticsearch] ) -> Elasticsearch: + if isinstance(es_client, tuple): + es_client = list(es_client) if not isinstance(es_client, Elasticsearch): - es_client = Elasticsearch(es_client) + es_client = Elasticsearch(es_client) # type: ignore[arg-type] return es_client @@ -334,16 +336,3 @@ def es_version(es_client: Elasticsearch) -> Tuple[int, int, int]: else: eland_es_version = es_client._eland_es_version # type: ignore return eland_es_version - - -def es_api_compat( - method: Callable[..., Dict[str, Any]], **kwargs: Any -) -> Dict[str, Any]: - """Expands the 'body' parameter to top-level parameters - on clients that would raise DeprecationWarnings if used. - """ - if ES_CLIENT_HAS_V8_0_DEPRECATIONS: - body = kwargs.pop("body", None) - if body: - kwargs.update(body) - return method(**kwargs) diff --git a/eland/dataframe.py b/eland/dataframe.py index d6857bd..86f3cc9 100644 --- a/eland/dataframe.py +++ b/eland/dataframe.py @@ -56,7 +56,7 @@ class DataFrame(NDFrame): Parameters ---------- - es_client: Elasticsearch client argument(s) (e.g. 'localhost:9200') + es_client: Elasticsearch client argument(s) (e.g. 'http://localhost:9200') - elasticsearch-py parameters or - elasticsearch-py instance es_index_pattern: str @@ -74,7 +74,7 @@ class DataFrame(NDFrame): -------- Constructing DataFrame from an Elasticsearch configuration arguments and an Elasticsearch index - >>> df = ed.DataFrame('localhost:9200', 'flights') + >>> df = ed.DataFrame('http://localhost:9200', 'flights') >>> df.head() AvgTicketPrice Cancelled ... dayOfWeek timestamp 0 841.265642 False ... 0 2018-01-01 00:00:00 @@ -89,7 +89,7 @@ class DataFrame(NDFrame): Constructing DataFrame from an Elasticsearch client and an Elasticsearch index >>> from elasticsearch import Elasticsearch - >>> es = Elasticsearch("localhost:9200") + >>> es = Elasticsearch("http://localhost:9200") >>> df = ed.DataFrame(es_client=es, es_index_pattern='flights', columns=['AvgTicketPrice', 'Cancelled']) >>> df.head() AvgTicketPrice Cancelled @@ -106,7 +106,7 @@ class DataFrame(NDFrame): (TODO - currently index_field must also be a field if not _id) >>> df = ed.DataFrame( - ... es_client='localhost', + ... es_client='http://localhost:9200', ... es_index_pattern='flights', ... columns=['AvgTicketPrice', 'timestamp'], ... es_index_field='timestamp' @@ -170,7 +170,7 @@ class DataFrame(NDFrame): Examples -------- - >>> df = ed.DataFrame('localhost', 'flights') + >>> df = ed.DataFrame('http://localhost:9200', 'flights') >>> assert isinstance(df.columns, pd.Index) >>> df.columns Index(['AvgTicketPrice', 'Cancelled', 'Carrier', 'Dest', 'DestAirportID', 'DestCityName', @@ -198,7 +198,7 @@ class DataFrame(NDFrame): Examples -------- - >>> df = ed.DataFrame('localhost', 'flights') + >>> df = ed.DataFrame('http://localhost:9200', 'flights') >>> df.empty False """ @@ -228,7 +228,7 @@ class DataFrame(NDFrame): Examples -------- - >>> df = ed.DataFrame('localhost', 'flights', columns=['Origin', 'Dest']) + >>> df = ed.DataFrame('http://localhost:9200', 'flights', columns=['Origin', 'Dest']) >>> df.head(3) Origin Dest 0 Frankfurt am Main Airport Sydney Kingsford Smith International Airport @@ -263,7 +263,7 @@ class DataFrame(NDFrame): Examples -------- - >>> df = ed.DataFrame('localhost', 'flights', columns=['Origin', 'Dest']) + >>> df = ed.DataFrame('http://localhost:9200', 'flights', columns=['Origin', 'Dest']) >>> df.tail() Origin \\ 13054 Pisa International Airport... @@ -365,7 +365,7 @@ class DataFrame(NDFrame): -------- Drop a column - >>> df = ed.DataFrame('localhost', 'ecommerce', columns=['customer_first_name', 'email', 'user']) + >>> df = ed.DataFrame('http://localhost:9200', 'ecommerce', columns=['customer_first_name', 'email', 'user']) >>> df.drop(columns=['user']) customer_first_name email 0 Eddie eddie@underwood-family.zzz @@ -575,7 +575,7 @@ class DataFrame(NDFrame): Examples -------- - >>> df = ed.DataFrame('localhost', 'ecommerce', columns=['customer_first_name', 'geoip.city_name']) + >>> df = ed.DataFrame('http://localhost:9200', 'ecommerce', columns=['customer_first_name', 'geoip.city_name']) >>> df.count() customer_first_name 4675 geoip.city_name 4094 @@ -597,7 +597,7 @@ class DataFrame(NDFrame): Examples -------- - >>> df = ed.DataFrame('localhost', 'flights') + >>> df = ed.DataFrame('http://localhost:9200', 'flights') >>> df = df[(df.OriginAirportID == 'AMS') & (df.FlightDelayMin > 60)] >>> df = df[['timestamp', 'OriginAirportID', 'DestAirportID', 'FlightDelayMin']] >>> df = df.tail() @@ -692,7 +692,7 @@ class DataFrame(NDFrame): Examples -------- - >>> df = ed.DataFrame("localhost:9200", "ecommerce") + >>> df = ed.DataFrame("http://localhost:9200", "ecommerce") >>> df.es_match("Men's", columns=["category"]) category currency ... type user 0 [Men's Clothing] EUR ... order eddie @@ -754,7 +754,7 @@ class DataFrame(NDFrame): .. _geo-distance query: https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-geo-distance-query.html - >>> df = ed.DataFrame('localhost', 'ecommerce', columns=['customer_first_name', 'geoip.city_name']) + >>> df = ed.DataFrame('http://localhost:9200', 'ecommerce', columns=['customer_first_name', 'geoip.city_name']) >>> df.es_query({"bool": {"filter": {"geo_distance": {"distance": "1km", "geoip.location": [55.3, 25.3]}}}}).head() customer_first_name geoip.city_name 1 Mary Dubai @@ -830,7 +830,7 @@ class DataFrame(NDFrame): Examples -------- - >>> df = ed.DataFrame('localhost', 'ecommerce', columns=['customer_first_name', 'geoip.city_name']) + >>> df = ed.DataFrame('http://localhost:9200', 'ecommerce', columns=['customer_first_name', 'geoip.city_name']) >>> df.info() Index: 4675 entries, 0 to 4674 @@ -1366,7 +1366,7 @@ class DataFrame(NDFrame): Examples -------- - >>> df = ed.DataFrame('localhost', 'flights', + >>> df = ed.DataFrame('http://localhost:9200', 'flights', ... columns=['AvgTicketPrice', 'Dest', 'Cancelled', 'timestamp', 'dayOfWeek']) >>> df.dtypes AvgTicketPrice float64 @@ -1407,7 +1407,7 @@ class DataFrame(NDFrame): Examples -------- - >>> df = ed.DataFrame('localhost', 'ecommerce') + >>> df = ed.DataFrame('http://localhost:9200', 'ecommerce') >>> df.shape (4675, 45) """ @@ -1462,7 +1462,7 @@ class DataFrame(NDFrame): Examples -------- - >>> df = ed.DataFrame('localhost:9200', 'flights', columns=['AvgTicketPrice', 'Cancelled']).head() + >>> df = ed.DataFrame('http://localhost:9200', 'flights', columns=['AvgTicketPrice', 'Cancelled']).head() >>> df AvgTicketPrice Cancelled 0 841.265642 False @@ -1520,7 +1520,7 @@ class DataFrame(NDFrame): Examples -------- - >>> df = ed.DataFrame('localhost:9200', 'flights', columns=['AvgTicketPrice', 'Cancelled']).head() + >>> df = ed.DataFrame('http://localhost:9200', 'flights', columns=['AvgTicketPrice', 'Cancelled']).head() >>> df AvgTicketPrice Cancelled 0 841.265642 False @@ -1614,7 +1614,7 @@ class DataFrame(NDFrame): Examples -------- - >>> df = ed.DataFrame('localhost', 'flights', columns=['AvgTicketPrice', 'DistanceKilometers', 'timestamp', 'DestCountry']) + >>> df = ed.DataFrame('http://localhost:9200', 'flights', columns=['AvgTicketPrice', 'DistanceKilometers', 'timestamp', 'DestCountry']) >>> df.aggregate(['sum', 'min', 'std'], numeric_only=True).astype(int) AvgTicketPrice DistanceKilometers sum 8204364 92616288 @@ -1689,7 +1689,7 @@ class DataFrame(NDFrame): Examples -------- - >>> ed_flights = ed.DataFrame('localhost', 'flights', columns=["AvgTicketPrice", "Cancelled", "dayOfWeek", "timestamp", "DestCountry"]) + >>> ed_flights = ed.DataFrame('http://localhost:9200', 'flights', columns=["AvgTicketPrice", "Cancelled", "dayOfWeek", "timestamp", "DestCountry"]) >>> ed_flights.groupby(["DestCountry", "Cancelled"]).agg(["min", "max"], numeric_only=True) # doctest: +NORMALIZE_WHITESPACE AvgTicketPrice dayOfWeek min max min max @@ -1784,7 +1784,7 @@ class DataFrame(NDFrame): Examples -------- - >>> ed_ecommerce = ed.DataFrame('localhost', 'ecommerce') + >>> ed_ecommerce = ed.DataFrame('http://localhost:9200', 'ecommerce') >>> ed_df = ed_ecommerce.filter(["total_quantity", "geoip.city_name", "customer_birth_date", "day_of_week", "taxful_total_price"]) >>> ed_df.mode(numeric_only=False) total_quantity geoip.city_name customer_birth_date day_of_week taxful_total_price @@ -1849,7 +1849,7 @@ class DataFrame(NDFrame): Examples -------- - >>> ed_df = ed.DataFrame('localhost', 'flights') + >>> ed_df = ed.DataFrame('http://localhost:9200', 'flights') >>> ed_flights = ed_df.filter(["AvgTicketPrice", "FlightDelayMin", "dayOfWeek", "timestamp"]) >>> ed_flights.quantile() # doctest: +SKIP AvgTicketPrice 640.387285 @@ -1892,7 +1892,7 @@ class DataFrame(NDFrame): Examples -------- - >>> ed_df = ed.DataFrame('localhost', 'flights') + >>> ed_df = ed.DataFrame('http://localhost:9200', 'flights') >>> ed_flights = ed_df.filter(["AvgTicketPrice", "FlightDelayMin", "dayOfWeek", "timestamp"]) >>> ed_flights.idxmax() AvgTicketPrice 1843 @@ -1924,7 +1924,7 @@ class DataFrame(NDFrame): Examples -------- - >>> ed_df = ed.DataFrame('localhost', 'flights') + >>> ed_df = ed.DataFrame('http://localhost:9200', 'flights') >>> ed_flights = ed_df.filter(["AvgTicketPrice", "FlightDelayMin", "dayOfWeek", "timestamp"]) >>> ed_flights.idxmin() AvgTicketPrice 5454 @@ -1960,7 +1960,7 @@ class DataFrame(NDFrame): Examples -------- - >>> df = ed.DataFrame('localhost', 'flights') + >>> df = ed.DataFrame('http://localhost:9200', 'flights') >>> df.shape (13059, 27) >>> df.query('FlightDelayMin > 60').shape @@ -2004,7 +2004,7 @@ class DataFrame(NDFrame): Examples -------- - >>> df = ed.DataFrame('localhost', 'flights') + >>> df = ed.DataFrame('http://localhost:9200', 'flights') >>> df.get('Carrier') 0 Kibana Airlines 1 Logstash Airways @@ -2135,7 +2135,7 @@ class DataFrame(NDFrame): Examples -------- - >>> ed_df = ed.DataFrame('localhost', 'flights', columns=['AvgTicketPrice', 'Carrier']).head(5) + >>> ed_df = ed.DataFrame('http://localhost:9200', 'flights', columns=['AvgTicketPrice', 'Carrier']).head(5) >>> pd_df = ed.eland_to_pandas(ed_df) >>> print(f"type(ed_df)={type(ed_df)}\\ntype(pd_df)={type(pd_df)}") type(ed_df)= diff --git a/eland/etl.py b/eland/etl.py index 3a60452..19b5b47 100644 --- a/eland/etl.py +++ b/eland/etl.py @@ -24,12 +24,7 @@ from elasticsearch import Elasticsearch from elasticsearch.helpers import parallel_bulk from eland import DataFrame -from eland.common import ( - DEFAULT_CHUNK_SIZE, - PANDAS_VERSION, - ensure_es_client, - es_api_compat, -) +from eland.common import DEFAULT_CHUNK_SIZE, PANDAS_VERSION, ensure_es_client from eland.field_mappings import FieldMappings, verify_mapping_compatibility try: @@ -128,7 +123,7 @@ def pandas_to_eland( >>> ed_df = ed.pandas_to_eland(pd_df, - ... 'localhost', + ... 'http://localhost:9200', ... 'pandas_to_eland', ... es_if_exists="replace", ... es_refresh=True, @@ -175,7 +170,7 @@ def pandas_to_eland( elif es_if_exists == "replace": es_client.indices.delete(index=es_dest_index) - es_api_compat(es_client.indices.create, index=es_dest_index, body=mapping) + es_client.indices.create(index=es_dest_index, mappings=mapping["mappings"]) elif es_if_exists == "append": dest_mapping = es_client.indices.get_mapping(index=es_dest_index)[ @@ -187,7 +182,7 @@ def pandas_to_eland( es_type_overrides=es_type_overrides, ) else: - es_api_compat(es_client.indices.create, index=es_dest_index, body=mapping) + es_client.indices.create(index=es_dest_index, mappings=mapping["mappings"]) def action_generator( pd_df: pd.DataFrame, @@ -252,7 +247,7 @@ def eland_to_pandas(ed_df: DataFrame, show_progress: bool = False) -> pd.DataFra Examples -------- - >>> ed_df = ed.DataFrame('localhost', 'flights').head() + >>> ed_df = ed.DataFrame('http://localhost:9200', 'flights').head() >>> type(ed_df) >>> ed_df @@ -282,7 +277,7 @@ def eland_to_pandas(ed_df: DataFrame, show_progress: bool = False) -> pd.DataFra Convert `eland.DataFrame` to `pandas.DataFrame` and show progress every 10000 rows - >>> pd_df = ed.eland_to_pandas(ed.DataFrame('localhost', 'flights'), show_progress=True) # doctest: +SKIP + >>> pd_df = ed.eland_to_pandas(ed.DataFrame('http://localhost:9200', 'flights'), show_progress=True) # doctest: +SKIP 2020-01-29 12:43:36.572395: read 10000 rows 2020-01-29 12:43:37.309031: read 13059 rows @@ -420,7 +415,7 @@ def csv_to_eland( # type: ignore >>> ed.csv_to_eland( ... "churn.csv", - ... es_client='localhost', + ... es_client='http://localhost:9200', ... es_dest_index='churn', ... es_refresh=True, ... index_col=0 diff --git a/eland/field_mappings.py b/eland/field_mappings.py index cb0936a..c7817a7 100644 --- a/eland/field_mappings.py +++ b/eland/field_mappings.py @@ -515,7 +515,7 @@ class FieldMappings: @staticmethod def _generate_es_mappings( dataframe: "pd.DataFrame", es_type_overrides: Optional[Mapping[str, str]] = None - ) -> Dict[str, Dict[str, Dict[str, Any]]]: + ) -> Dict[str, Dict[str, Any]]: """Given a pandas dataframe, generate the associated Elasticsearch mapping Parameters @@ -894,20 +894,20 @@ def verify_mapping_compatibility( problems = [] es_type_overrides = es_type_overrides or {} - ed_mapping = ed_mapping["mappings"]["properties"] - es_mapping = es_mapping["mappings"]["properties"] + ed_props = ed_mapping["mappings"]["properties"] + es_props = es_mapping["mappings"]["properties"] - for key in sorted(es_mapping.keys()): - if key not in ed_mapping: + for key in sorted(es_props.keys()): + if key not in ed_props: problems.append(f"- {key!r} is missing from DataFrame columns") - for key, key_def in sorted(ed_mapping.items()): - if key not in es_mapping: + for key, key_def in sorted(ed_props.items()): + if key not in es_props: problems.append(f"- {key!r} is missing from ES index mapping") continue key_type = es_type_overrides.get(key, key_def["type"]) - es_key_type = es_mapping[key]["type"] + es_key_type = es_props[key]["type"] if key_type != es_key_type and es_key_type not in ES_COMPATIBLE_TYPES.get( key_type, () ): diff --git a/eland/groupby.py b/eland/groupby.py index cb0a16a..316e658 100644 --- a/eland/groupby.py +++ b/eland/groupby.py @@ -68,7 +68,7 @@ class DataFrameGroupBy(GroupBy): Examples -------- >>> df = ed.DataFrame( - ... "localhost", "flights", + ... "http://localhost:9200", "flights", ... columns=["AvgTicketPrice", "Cancelled", "dayOfWeek", "timestamp", "DestCountry"] ... ) >>> df.groupby("DestCountry").mean(numeric_only=False) # doctest: +SKIP @@ -119,7 +119,7 @@ class DataFrameGroupBy(GroupBy): Examples -------- >>> df = ed.DataFrame( - ... "localhost", "flights", + ... "http://localhost:9200", "flights", ... columns=["AvgTicketPrice", "Cancelled", "dayOfWeek", "timestamp", "DestCountry"] ... ) >>> df.groupby("DestCountry").var() # doctest: +NORMALIZE_WHITESPACE @@ -170,7 +170,7 @@ class DataFrameGroupBy(GroupBy): Examples -------- >>> df = ed.DataFrame( - ... "localhost", "flights", + ... "http://localhost:9200", "flights", ... columns=["AvgTicketPrice", "Cancelled", "dayOfWeek", "DestCountry"] ... ) >>> df.groupby("DestCountry").std() # doctest: +NORMALIZE_WHITESPACE @@ -221,7 +221,7 @@ class DataFrameGroupBy(GroupBy): Examples -------- >>> df = ed.DataFrame( - ... "localhost", "flights", + ... "http://localhost:9200", "flights", ... columns=["AvgTicketPrice", "Cancelled", "dayOfWeek", "timestamp", "DestCountry"] ... ) >>> df.groupby("DestCountry").mad() # doctest: +SKIP @@ -272,7 +272,7 @@ class DataFrameGroupBy(GroupBy): Examples -------- >>> df = ed.DataFrame( - ... "localhost", "flights", + ... "http://localhost:9200", "flights", ... columns=["AvgTicketPrice", "Cancelled", "dayOfWeek", "timestamp", "DestCountry"] ... ) >>> df.groupby("DestCountry").median(numeric_only=False) # doctest: +SKIP @@ -323,7 +323,7 @@ class DataFrameGroupBy(GroupBy): Examples -------- >>> df = ed.DataFrame( - ... "localhost", "flights", + ... "http://localhost:9200", "flights", ... columns=["AvgTicketPrice", "Cancelled", "dayOfWeek", "DestCountry"] ... ) >>> df.groupby("DestCountry").sum() # doctest: +NORMALIZE_WHITESPACE @@ -374,7 +374,7 @@ class DataFrameGroupBy(GroupBy): Examples -------- >>> df = ed.DataFrame( - ... "localhost", "flights", + ... "http://localhost:9200", "flights", ... columns=["AvgTicketPrice", "Cancelled", "dayOfWeek", "timestamp", "DestCountry"] ... ) >>> df.groupby("DestCountry").min(numeric_only=False) # doctest: +NORMALIZE_WHITESPACE @@ -425,7 +425,7 @@ class DataFrameGroupBy(GroupBy): Examples -------- >>> df = ed.DataFrame( - ... "localhost", "flights", + ... "http://localhost:9200", "flights", ... columns=["AvgTicketPrice", "Cancelled", "dayOfWeek", "timestamp", "DestCountry"] ... ) >>> df.groupby("DestCountry").max(numeric_only=False) # doctest: +NORMALIZE_WHITESPACE @@ -476,7 +476,7 @@ class DataFrameGroupBy(GroupBy): Examples -------- >>> df = ed.DataFrame( - ... "localhost", "flights", + ... "http://localhost:9200", "flights", ... columns=["AvgTicketPrice", "Cancelled", "dayOfWeek", "DestCountry"] ... ) >>> df.groupby("DestCountry").nunique() # doctest: +NORMALIZE_WHITESPACE @@ -526,7 +526,7 @@ class DataFrameGroupBy(GroupBy): Examples -------- - >>> ed_df = ed.DataFrame('localhost', 'flights') + >>> ed_df = ed.DataFrame('http://localhost:9200', 'flights') >>> ed_flights = ed_df.filter(["AvgTicketPrice", "FlightDelayMin", "dayOfWeek", "timestamp"]) >>> ed_flights.groupby(["dayOfWeek", "Cancelled"]).quantile() # doctest: +SKIP AvgTicketPrice FlightDelayMin @@ -616,7 +616,7 @@ class DataFrameGroupBy(GroupBy): Examples -------- >>> df = ed.DataFrame( - ... "localhost", "flights", + ... "http://localhost:9200", "flights", ... columns=["AvgTicketPrice", "Cancelled", "dayOfWeek", "DestCountry"] ... ) >>> df.groupby("DestCountry").aggregate(["min", "max"]) # doctest: +NORMALIZE_WHITESPACE @@ -670,7 +670,7 @@ class DataFrameGroupBy(GroupBy): Examples -------- >>> df = ed.DataFrame( - ... "localhost", "flights", + ... "http://localhost:9200", "flights", ... columns=["AvgTicketPrice", "Cancelled", "dayOfWeek", "DestCountry"] ... ) >>> df.groupby("DestCountry").count() # doctest: +NORMALIZE_WHITESPACE diff --git a/eland/ml/ml_model.py b/eland/ml/ml_model.py index 04c6d7c..e4f8d9e 100644 --- a/eland/ml/ml_model.py +++ b/eland/ml/ml_model.py @@ -79,7 +79,7 @@ class MLModel: model_id: str The unique identifier of the trained inference model in Elasticsearch. """ - self._client = ensure_es_client(es_client) + self._client: Elasticsearch = ensure_es_client(es_client) self._model_id = model_id self._trained_model_config_cache: Optional[Dict[str, Any]] = None @@ -120,7 +120,7 @@ class MLModel: >>> # Serialise the model to Elasticsearch >>> feature_names = ["f0", "f1", "f2", "f3", "f4", "f5"] >>> model_id = "test_xgb_regressor" - >>> es_model = MLModel.import_model('localhost', model_id, regressor, feature_names, es_if_exists='replace') + >>> es_model = MLModel.import_model('http://localhost:9200', model_id, regressor, feature_names, es_if_exists='replace') >>> # Get some test results from Elasticsearch model >>> es_model.predict(test_data) # doctest: +SKIP @@ -167,20 +167,18 @@ class MLModel: ) results = self._client.ingest.simulate( - body={ - "pipeline": { - "processors": [ - { - "inference": { - "model_id": self._model_id, - "inference_config": {self.model_type: {}}, - field_map_name: {}, - } + pipeline={ + "processors": [ + { + "inference": { + "model_id": self._model_id, + "inference_config": {self.model_type: {}}, + field_map_name: {}, } - ] - }, - "docs": docs, - } + } + ] + }, + docs=docs, ) # Unpack results into an array. Errors can be present @@ -342,7 +340,7 @@ class MLModel: >>> feature_names = ["f0", "f1", "f2", "f3", "f4"] >>> model_id = "test_decision_tree_classifier" >>> es_model = MLModel.import_model( - ... 'localhost', + ... 'http://localhost:9200', ... model_id=model_id, ... model=classifier, ... feature_names=feature_names, @@ -383,22 +381,21 @@ class MLModel: elif es_if_exists == "replace": ml_model.delete_model() - body: Dict[str, Any] = { - "input": {"field_names": feature_names}, - } - # 'inference_config' is required in 7.8+ but isn't available in <=7.7 - if es_version(es_client) >= (7, 8): - body["inference_config"] = {model_type: {}} - if es_compress_model_definition: - body["compressed_definition"] = serializer.serialize_and_compress_model() + ml_model._client.ml.put_trained_model( + model_id=model_id, + input={"field_names": feature_names}, + inference_config={model_type: {}}, + compressed_definition=serializer.serialize_and_compress_model(), + ) else: - body["definition"] = serializer.serialize_model() + ml_model._client.ml.put_trained_model( + model_id=model_id, + input={"field_names": feature_names}, + inference_config={model_type: {}}, + definition=serializer.serialize_model(), + ) - ml_model._client.ml.put_trained_model( - model_id=model_id, - body=body, - ) return ml_model def delete_model(self) -> None: @@ -408,7 +405,9 @@ class MLModel: If model doesn't exist, ignore failure. """ try: - self._client.ml.delete_trained_model(model_id=self._model_id, ignore=(404,)) + self._client.options(ignore_status=404).ml.delete_trained_model( + model_id=self._model_id + ) except elasticsearch.NotFoundError: pass @@ -426,16 +425,7 @@ class MLModel: def _trained_model_config(self) -> Dict[str, Any]: """Lazily loads an ML models 'trained_model_config' information""" if self._trained_model_config_cache is None: - - # In Elasticsearch 7.7 and earlier you can't get - # target type without pulling the model definition - # so we check the version first. - if es_version(self._client) < (7, 8): - resp = self._client.ml.get_trained_models( - model_id=self._model_id, include_model_definition=True - ) - else: - resp = self._client.ml.get_trained_models(model_id=self._model_id) + resp = self._client.ml.get_trained_models(model_id=self._model_id) if resp["count"] > 1: raise ValueError(f"Model ID {self._model_id!r} wasn't unambiguous") diff --git a/eland/ml/pytorch/_pytorch_model.py b/eland/ml/pytorch/_pytorch_model.py index 0797e73..77d6eb7 100644 --- a/eland/ml/pytorch/_pytorch_model.py +++ b/eland/ml/pytorch/_pytorch_model.py @@ -46,21 +46,19 @@ class PyTorchModel: es_client: Union[str, List[str], Tuple[str, ...], "Elasticsearch"], model_id: str, ): - self._client = ensure_es_client(es_client) + self._client: Elasticsearch = ensure_es_client(es_client) self.model_id = model_id def put_config(self, path: str) -> None: with open(path) as f: config = json.load(f) - self._client.ml.put_trained_model(model_id=self.model_id, body=config) + self._client.ml.put_trained_model(model_id=self.model_id, **config) def put_vocab(self, path: str) -> None: with open(path) as f: vocab = json.load(f) - self._client.transport.perform_request( - "PUT", - f"/_ml/trained_models/{self.model_id}/vocabulary", - body=vocab, + self._client.ml.put_trained_model_vocabulary( + model_id=self.model_id, vocabulary=vocab["vocabulary"] ) def put_model(self, model_path: str, chunk_size: int = DEFAULT_CHUNK_SIZE) -> None: @@ -76,15 +74,12 @@ class PyTorchModel: yield base64.b64encode(data).decode() for i, data in tqdm(enumerate(model_file_chunk_generator()), total=total_parts): - body = { - "total_definition_length": model_size, - "total_parts": total_parts, - "definition": data, - } - self._client.transport.perform_request( - "PUT", - f"/_ml/trained_models/{self.model_id}/definition/{i}", - body=body, + self._client.ml.put_trained_model_definition_part( + model_id=self.model_id, + part=i, + total_definition_length=model_size, + total_parts=total_parts, + definition=data, ) def import_model( @@ -100,42 +95,41 @@ class PyTorchModel: self.put_vocab(vocab_path) def infer( - self, body: Dict[str, Any], timeout: str = DEFAULT_TIMEOUT - ) -> Union[bool, Any]: - return self._client.transport.perform_request( - "POST", - f"/_ml/trained_models/{self.model_id}/deployment/_infer", - body=body, - params={"timeout": timeout, "request_timeout": 60}, + self, + docs: List[Dict[str, str]], + timeout: str = DEFAULT_TIMEOUT, + ) -> Any: + return self._client.options( + request_timeout=60 + ).ml.infer_trained_model_deployment( + model_id=self.model_id, + timeout=timeout, + docs=docs, ) def start(self, timeout: str = DEFAULT_TIMEOUT) -> None: - self._client.transport.perform_request( - "POST", - f"/_ml/trained_models/{self.model_id}/deployment/_start", - params={"timeout": timeout, "request_timeout": 60, "wait_for": "started"}, + self._client.options(request_timeout=60).ml.start_trained_model_deployment( + model_id=self.model_id, timeout=timeout, wait_for="started" ) def stop(self) -> None: - self._client.transport.perform_request( - "POST", - f"/_ml/trained_models/{self.model_id}/deployment/_stop", - params={"ignore": 404}, - ) + self._client.ml.stop_trained_model_deployment(model_id=self.model_id) def delete(self) -> None: - self._client.ml.delete_trained_model(model_id=self.model_id, ignore=(404,)) + self._client.options(ignore_status=404).ml.delete_trained_model( + model_id=self.model_id + ) @classmethod def list( cls, es_client: Union[str, List[str], Tuple[str, ...], "Elasticsearch"] ) -> Set[str]: client = ensure_es_client(es_client) - res = client.ml.get_trained_models(model_id="*", allow_no_match=True) + resp = client.ml.get_trained_models(model_id="*", allow_no_match=True) return set( [ model["model_id"] - for model in res["trained_model_configs"] + for model in resp["trained_model_configs"] if model["model_type"] == "pytorch" ] ) diff --git a/eland/ndframe.py b/eland/ndframe.py index 6769293..938c75c 100644 --- a/eland/ndframe.py +++ b/eland/ndframe.py @@ -99,7 +99,7 @@ class NDFrame(ABC): Examples -------- - >>> df = ed.DataFrame('localhost', 'flights') + >>> df = ed.DataFrame('http://localhost:9200', 'flights') >>> assert isinstance(df.index, ed.Index) >>> df.index.es_index_field '_id' @@ -127,7 +127,7 @@ class NDFrame(ABC): Examples -------- - >>> df = ed.DataFrame('localhost', 'flights', columns=['Origin', 'AvgTicketPrice', 'timestamp', 'dayOfWeek']) + >>> df = ed.DataFrame('http://localhost:9200', 'flights', columns=['Origin', 'AvgTicketPrice', 'timestamp', 'dayOfWeek']) >>> df.dtypes Origin object AvgTicketPrice float64 @@ -149,7 +149,7 @@ class NDFrame(ABC): Examples -------- - >>> df = ed.DataFrame('localhost', 'flights', columns=['Origin', 'AvgTicketPrice', 'timestamp', 'dayOfWeek']) + >>> df = ed.DataFrame('http://localhost:9200', 'flights', columns=['Origin', 'AvgTicketPrice', 'timestamp', 'dayOfWeek']) >>> df.es_dtypes Origin keyword AvgTicketPrice float @@ -213,7 +213,7 @@ class NDFrame(ABC): Examples -------- - >>> df = ed.DataFrame('localhost', 'flights', columns=["AvgTicketPrice", "Cancelled", "dayOfWeek", "timestamp", "DestCountry"]) + >>> df = ed.DataFrame('http://localhost:9200', 'flights', columns=["AvgTicketPrice", "Cancelled", "dayOfWeek", "timestamp", "DestCountry"]) >>> df.mean() # doctest: +SKIP AvgTicketPrice 628.254 Cancelled 0.128494 @@ -262,7 +262,7 @@ class NDFrame(ABC): Examples -------- - >>> df = ed.DataFrame('localhost', 'flights', columns=["AvgTicketPrice", "Cancelled", "dayOfWeek", "timestamp", "DestCountry"]) + >>> df = ed.DataFrame('http://localhost:9200', 'flights', columns=["AvgTicketPrice", "Cancelled", "dayOfWeek", "timestamp", "DestCountry"]) >>> df.sum() # doctest: +SKIP AvgTicketPrice 8.20436e+06 Cancelled 1678 @@ -310,7 +310,7 @@ class NDFrame(ABC): Examples -------- - >>> df = ed.DataFrame('localhost', 'flights', columns=["AvgTicketPrice", "Cancelled", "dayOfWeek", "timestamp", "DestCountry"]) + >>> df = ed.DataFrame('http://localhost:9200', 'flights', columns=["AvgTicketPrice", "Cancelled", "dayOfWeek", "timestamp", "DestCountry"]) >>> df.min() # doctest: +SKIP AvgTicketPrice 100.021 Cancelled False @@ -357,7 +357,7 @@ class NDFrame(ABC): Examples -------- - >>> df = ed.DataFrame('localhost', 'flights', columns=["AvgTicketPrice", "Cancelled", "dayOfWeek", "timestamp", "DestCountry"]) + >>> df = ed.DataFrame('http://localhost:9200', 'flights', columns=["AvgTicketPrice", "Cancelled", "dayOfWeek", "timestamp", "DestCountry"]) >>> df.var() # doctest: +SKIP AvgTicketPrice 70964.570234 Cancelled 0.111987 @@ -403,7 +403,7 @@ class NDFrame(ABC): Examples -------- - >>> df = ed.DataFrame('localhost', 'flights', columns=["AvgTicketPrice", "Cancelled", "dayOfWeek", "timestamp", "DestCountry"]) + >>> df = ed.DataFrame('http://localhost:9200', 'flights', columns=["AvgTicketPrice", "Cancelled", "dayOfWeek", "timestamp", "DestCountry"]) >>> df.std() # doctest: +SKIP AvgTicketPrice 266.407061 Cancelled 0.334664 @@ -449,7 +449,7 @@ class NDFrame(ABC): Examples -------- - >>> df = ed.DataFrame('localhost', 'flights', columns=["AvgTicketPrice", "Cancelled", "dayOfWeek", "timestamp", "DestCountry"]) + >>> df = ed.DataFrame('http://localhost:9200', 'flights', columns=["AvgTicketPrice", "Cancelled", "dayOfWeek", "timestamp", "DestCountry"]) >>> df.median() # doctest: +SKIP AvgTicketPrice 640.363 Cancelled False @@ -498,7 +498,7 @@ class NDFrame(ABC): Examples -------- - >>> df = ed.DataFrame('localhost', 'flights', columns=["AvgTicketPrice", "Cancelled", "dayOfWeek", "timestamp", "DestCountry"]) + >>> df = ed.DataFrame('http://localhost:9200', 'flights', columns=["AvgTicketPrice", "Cancelled", "dayOfWeek", "timestamp", "DestCountry"]) >>> df.max() # doctest: +SKIP AvgTicketPrice 1199.73 Cancelled True @@ -557,7 +557,7 @@ class NDFrame(ABC): Examples -------- >>> columns = ['category', 'currency', 'customer_birth_date', 'customer_first_name', 'user'] - >>> df = ed.DataFrame('localhost', 'ecommerce', columns=columns) + >>> df = ed.DataFrame('http://localhost:9200', 'ecommerce', columns=columns) >>> df.nunique() category 6 currency 1 @@ -583,7 +583,7 @@ class NDFrame(ABC): Examples -------- - >>> df = ed.DataFrame('localhost', 'flights', columns=["AvgTicketPrice", "Cancelled", "dayOfWeek", "timestamp", "DestCountry"]) + >>> df = ed.DataFrame('http://localhost:9200', 'flights', columns=["AvgTicketPrice", "Cancelled", "dayOfWeek", "timestamp", "DestCountry"]) >>> df.mad() # doctest: +SKIP AvgTicketPrice 213.35497 dayOfWeek 2.00000 @@ -628,7 +628,7 @@ class NDFrame(ABC): Examples -------- - >>> df = ed.DataFrame('localhost', 'flights', columns=['AvgTicketPrice', 'FlightDelayMin']) # ignoring percentiles + >>> df = ed.DataFrame('http://localhost:9200', 'flights', columns=['AvgTicketPrice', 'FlightDelayMin']) # ignoring percentiles >>> df.describe() # doctest: +SKIP AvgTicketPrice FlightDelayMin count 13059.000000 13059.000000 diff --git a/eland/operations.py b/eland/operations.py index 8ac8bed..15c0174 100644 --- a/eland/operations.py +++ b/eland/operations.py @@ -34,7 +34,6 @@ from typing import ( import numpy as np import pandas as pd # type: ignore -from elasticsearch.exceptions import NotFoundError from eland.actions import PostProcessingAction from eland.common import ( @@ -45,8 +44,6 @@ from eland.common import ( SortOrder, build_pd_series, elasticsearch_date_to_pandas_date, - es_api_compat, - es_version, ) from eland.index import Index from eland.query import Query @@ -173,7 +170,7 @@ class Operations: body.exists(field, must=True) field_exists_count = query_compiler._client.count( - index=query_compiler._index_pattern, body=body.to_count_body() + index=query_compiler._index_pattern, **body.to_count_body() )["count"] counts[field] = field_exists_count @@ -240,7 +237,7 @@ class Operations: # Fetch Response response = query_compiler._client.search( - index=query_compiler._index_pattern, size=0, body=body.to_search_body() + index=query_compiler._index_pattern, size=0, **body.to_search_body() ) response = response["aggregations"] @@ -404,7 +401,7 @@ class Operations: ) response = query_compiler._client.search( - index=query_compiler._index_pattern, size=0, body=body.to_search_body() + index=query_compiler._index_pattern, size=0, **body.to_search_body() ) """ @@ -1275,7 +1272,7 @@ class Operations: body.exists(field, must=True) count: int = query_compiler._client.count( - index=query_compiler._index_pattern, body=body.to_count_body() + index=query_compiler._index_pattern, **body.to_count_body() )["count"] return count @@ -1313,7 +1310,7 @@ class Operations: body.terms(field, items, must=True) count: int = query_compiler._client.count( - index=query_compiler._index_pattern, body=body.to_count_body() + index=query_compiler._index_pattern, **body.to_count_body() )["count"] return count @@ -1488,99 +1485,16 @@ def _search_yield_hits( [[{'_index': 'flights', '_type': '_doc', '_id': '0', '_score': None, '_source': {...}, 'sort': [...]}, {'_index': 'flights', '_type': '_doc', '_id': '1', '_score': None, '_source': {...}, 'sort': [...]}]] """ + # No documents, no reason to send a search. + if max_number_of_hits == 0: + return + # Make a copy of 'body' to avoid mutating it outside this function. body = body.copy() # Use the default search size body.setdefault("size", DEFAULT_SEARCH_SIZE) - # Elasticsearch 7.12 added '_shard_doc' sort tiebreaker for PITs which - # means we're guaranteed to be safe on documents with a duplicate sort rank. - if es_version(query_compiler._client) >= (7, 12, 0): - yield from _search_with_pit_and_search_after( - query_compiler=query_compiler, - body=body, - max_number_of_hits=max_number_of_hits, - ) - - # Otherwise we use 'scroll' like we used to. - else: - yield from _search_with_scroll( - query_compiler=query_compiler, - body=body, - max_number_of_hits=max_number_of_hits, - ) - - -def _search_with_scroll( - query_compiler: "QueryCompiler", - body: Dict[str, Any], - max_number_of_hits: Optional[int], -) -> Generator[List[Dict[str, Any]], None, None]: - # No documents, no reason to send a search. - if max_number_of_hits == 0: - return - - client = query_compiler._client - hits_yielded = 0 - - # Make the initial search with 'scroll' set - resp = es_api_compat( - client.search, - index=query_compiler._index_pattern, - body=body, - scroll=DEFAULT_PIT_KEEP_ALIVE, - ) - scroll_id: Optional[str] = resp.get("_scroll_id", None) - - try: - while scroll_id and ( - max_number_of_hits is None or hits_yielded < max_number_of_hits - ): - hits: List[Dict[str, Any]] = resp["hits"]["hits"] - - # If we didn't receive any hits it means we've reached the end. - if not hits: - break - - # Calculate which hits should be yielded from this batch - if max_number_of_hits is None: - hits_to_yield = len(hits) - else: - hits_to_yield = min(len(hits), max_number_of_hits - hits_yielded) - - # Yield the hits we need to and then track the total number. - # Never yield an empty list as that makes things simpler for - # downstream consumers. - if hits and hits_to_yield > 0: - yield hits[:hits_to_yield] - hits_yielded += hits_to_yield - - # Retrieve the next set of results - resp = client.scroll( - body={"scroll_id": scroll_id, "scroll": DEFAULT_PIT_KEEP_ALIVE}, - ) - scroll_id = resp.get("_scroll_id", None) # Update the scroll ID. - - finally: - # Close the scroll if we have one open - if scroll_id is not None: - try: - client.clear_scroll(body={"scroll_id": [scroll_id]}) - except NotFoundError: - pass - - -def _search_with_pit_and_search_after( - query_compiler: "QueryCompiler", - body: Dict[str, Any], - max_number_of_hits: Optional[int], -) -> Generator[List[Dict[str, Any]], None, None]: - - # No documents, no reason to send a search. - if max_number_of_hits == 0: - return - client = query_compiler._client hits_yielded = 0 # Track the total number of hits yielded. pit_id: Optional[str] = None @@ -1603,7 +1517,7 @@ def _search_with_pit_and_search_after( body["pit"] = {"id": pit_id, "keep_alive": DEFAULT_PIT_KEEP_ALIVE} while max_number_of_hits is None or hits_yielded < max_number_of_hits: - resp = es_api_compat(client.search, body=body) + resp = client.search(**body) hits: List[Dict[str, Any]] = resp["hits"]["hits"] # The point in time ID can change between searches so we @@ -1636,8 +1550,4 @@ def _search_with_pit_and_search_after( # We want to cleanup the point in time if we allocated one # to keep our memory footprint low. if pit_id is not None: - try: - client.close_point_in_time(body={"id": pit_id}) - except NotFoundError: - # If a point in time is already closed Elasticsearch throws NotFoundError - pass + client.options(ignore_status=404).close_point_in_time(id=pit_id) diff --git a/eland/plotting/_core.py b/eland/plotting/_core.py index 2f402fe..f4f868c 100644 --- a/eland/plotting/_core.py +++ b/eland/plotting/_core.py @@ -43,7 +43,7 @@ def ed_hist_series( Examples -------- >>> import matplotlib.pyplot as plt - >>> df = ed.DataFrame('localhost', 'flights') + >>> df = ed.DataFrame('http://localhost:9200', 'flights') >>> df[df.OriginWeather == 'Sunny']['FlightTimeMin'].hist(alpha=0.5, density=True) # doctest: +SKIP >>> df[df.OriginWeather != 'Sunny']['FlightTimeMin'].hist(alpha=0.5, density=True) # doctest: +SKIP >>> plt.show() # doctest: +SKIP @@ -109,7 +109,7 @@ def ed_hist_frame( Examples -------- - >>> df = ed.DataFrame('localhost', 'flights') + >>> df = ed.DataFrame('http://localhost:9200', 'flights') >>> hist = df.select_dtypes(include=[np.number]).hist(figsize=[10,10]) # doctest: +SKIP """ return hist_frame( diff --git a/eland/series.py b/eland/series.py index 9a29e30..4a02b79 100644 --- a/eland/series.py +++ b/eland/series.py @@ -97,7 +97,7 @@ class Series(NDFrame): Examples -------- - >>> ed.Series(es_client='localhost', es_index_pattern='flights', name='Carrier') + >>> ed.Series(es_client='http://localhost:9200', es_index_pattern='flights', name='Carrier') 0 Kibana Airlines 1 Logstash Airways 2 Logstash Airways @@ -165,7 +165,7 @@ class Series(NDFrame): Examples -------- - >>> df = ed.Series('localhost', 'ecommerce', name='total_quantity') + >>> df = ed.Series('http://localhost:9200', 'ecommerce', name='total_quantity') >>> df.shape (4675, 1) """ @@ -214,7 +214,7 @@ class Series(NDFrame): Examples -------- - >>> df = ed.DataFrame('localhost', 'flights') + >>> df = ed.DataFrame('http://localhost:9200', 'flights') >>> df.Carrier 0 Kibana Airlines 1 Logstash Airways @@ -290,7 +290,7 @@ class Series(NDFrame): Examples -------- - >>> df = ed.DataFrame('localhost', 'flights') + >>> df = ed.DataFrame('http://localhost:9200', 'flights') >>> df['Carrier'].value_counts() Logstash Airways 3331 JetBeats 3274 @@ -587,7 +587,7 @@ class Series(NDFrame): Examples -------- - >>> ed_flights = ed.DataFrame('localhost', 'flights') + >>> ed_flights = ed.DataFrame('http://localhost:9200', 'flights') >>> ed_flights["timestamp"].quantile([.2,.5,.75]) # doctest: +SKIP 0.20 2018-01-09 04:30:57.289159912 0.50 2018-01-21 23:39:27.031627441 @@ -691,7 +691,7 @@ class Series(NDFrame): Examples -------- - >>> ed_ecommerce = ed.DataFrame('localhost', 'ecommerce') + >>> ed_ecommerce = ed.DataFrame('http://localhost:9200', 'ecommerce') >>> ed_ecommerce["day_of_week"].mode() 0 Thursday dtype: object @@ -760,7 +760,7 @@ class Series(NDFrame): Examples -------- >>> df = ed.DataFrame( - ... "localhost:9200", "ecommerce", + ... "http://localhost:9200", "ecommerce", ... columns=["category", "taxful_total_price"] ... ) >>> df[ @@ -807,7 +807,7 @@ class Series(NDFrame): Examples -------- - >>> df = ed.DataFrame('localhost', 'ecommerce').head(5) + >>> df = ed.DataFrame('http://localhost:9200', 'ecommerce').head(5) >>> df.taxful_total_price 0 36.98 1 53.98 @@ -867,7 +867,7 @@ class Series(NDFrame): Examples -------- - >>> df = ed.DataFrame('localhost', 'ecommerce').head(5) + >>> df = ed.DataFrame('http://localhost:9200', 'ecommerce').head(5) >>> df.taxful_total_price 0 36.98 1 53.98 @@ -906,7 +906,7 @@ class Series(NDFrame): Examples -------- - >>> df = ed.DataFrame('localhost', 'ecommerce').head(5) + >>> df = ed.DataFrame('http://localhost:9200', 'ecommerce').head(5) >>> df.taxful_total_price 0 36.98 1 53.98 @@ -945,7 +945,7 @@ class Series(NDFrame): Examples -------- - >>> df = ed.DataFrame('localhost', 'ecommerce').head(5) + >>> df = ed.DataFrame('http://localhost:9200', 'ecommerce').head(5) >>> df.taxful_total_price 0 36.98 1 53.98 @@ -984,7 +984,7 @@ class Series(NDFrame): Examples -------- - >>> df = ed.DataFrame('localhost', 'ecommerce').head(5) + >>> df = ed.DataFrame('http://localhost:9200', 'ecommerce').head(5) >>> df.taxful_total_price 0 36.98 1 53.98 @@ -1023,7 +1023,7 @@ class Series(NDFrame): Examples -------- - >>> df = ed.DataFrame('localhost', 'ecommerce').head(5) + >>> df = ed.DataFrame('http://localhost:9200', 'ecommerce').head(5) >>> df.taxful_total_price 0 36.98 1 53.98 @@ -1062,7 +1062,7 @@ class Series(NDFrame): Examples -------- - >>> df = ed.DataFrame('localhost', 'ecommerce').head(5) + >>> df = ed.DataFrame('http://localhost:9200', 'ecommerce').head(5) >>> df.taxful_total_price 0 36.98 1 53.98 @@ -1101,7 +1101,7 @@ class Series(NDFrame): Examples -------- - >>> df = ed.DataFrame('localhost', 'ecommerce').head(5) + >>> df = ed.DataFrame('http://localhost:9200', 'ecommerce').head(5) >>> df.taxful_total_price 0 36.98 1 53.98 @@ -1133,7 +1133,7 @@ class Series(NDFrame): Examples -------- - >>> df = ed.DataFrame('localhost', 'ecommerce').head(5) + >>> df = ed.DataFrame('http://localhost:9200', 'ecommerce').head(5) >>> df.taxful_total_price 0 36.98 1 53.98 @@ -1165,7 +1165,7 @@ class Series(NDFrame): Examples -------- - >>> df = ed.DataFrame('localhost', 'ecommerce').head(5) + >>> df = ed.DataFrame('http://localhost:9200', 'ecommerce').head(5) >>> df.taxful_total_price 0 36.98 1 53.98 @@ -1197,7 +1197,7 @@ class Series(NDFrame): Examples -------- - >>> df = ed.DataFrame('localhost', 'ecommerce').head(5) + >>> df = ed.DataFrame('http://localhost:9200', 'ecommerce').head(5) >>> df.taxful_total_price 0 36.98 1 53.98 @@ -1229,7 +1229,7 @@ class Series(NDFrame): Examples -------- - >>> df = ed.DataFrame('localhost', 'ecommerce').head(5) + >>> df = ed.DataFrame('http://localhost:9200', 'ecommerce').head(5) >>> df.taxful_total_price 0 36.98 1 53.98 @@ -1261,7 +1261,7 @@ class Series(NDFrame): Examples -------- - >>> df = ed.DataFrame('localhost', 'ecommerce').head(5) + >>> df = ed.DataFrame('http://localhost:9200', 'ecommerce').head(5) >>> df.total_quantity 0 2 1 2 @@ -1293,7 +1293,7 @@ class Series(NDFrame): Examples -------- - >>> df = ed.DataFrame('localhost', 'ecommerce').head(5) + >>> df = ed.DataFrame('http://localhost:9200', 'ecommerce').head(5) >>> df.taxful_total_price 0 36.98 1 53.98 @@ -1415,7 +1415,7 @@ class Series(NDFrame): Examples -------- - >>> s = ed.DataFrame('localhost', 'flights')['AvgTicketPrice'] + >>> s = ed.DataFrame('http://localhost:9200', 'flights')['AvgTicketPrice'] >>> int(s.max()) 1199 """ @@ -1439,7 +1439,7 @@ class Series(NDFrame): Examples -------- - >>> s = ed.DataFrame('localhost', 'flights')['AvgTicketPrice'] + >>> s = ed.DataFrame('http://localhost:9200', 'flights')['AvgTicketPrice'] >>> int(s.mean()) 628 """ @@ -1463,7 +1463,7 @@ class Series(NDFrame): Examples -------- - >>> s = ed.DataFrame('localhost', 'flights')['AvgTicketPrice'] + >>> s = ed.DataFrame('http://localhost:9200', 'flights')['AvgTicketPrice'] >>> int(s.median()) 640 """ @@ -1487,7 +1487,7 @@ class Series(NDFrame): Examples -------- - >>> s = ed.DataFrame('localhost', 'flights')['AvgTicketPrice'] + >>> s = ed.DataFrame('http://localhost:9200', 'flights')['AvgTicketPrice'] >>> int(s.min()) 100 """ @@ -1511,7 +1511,7 @@ class Series(NDFrame): Examples -------- - >>> s = ed.DataFrame('localhost', 'flights')['AvgTicketPrice'] + >>> s = ed.DataFrame('http://localhost:9200', 'flights')['AvgTicketPrice'] >>> int(s.sum()) 8204364 """ @@ -1533,7 +1533,7 @@ class Series(NDFrame): Examples -------- - >>> s = ed.DataFrame('localhost', 'flights')['Carrier'] + >>> s = ed.DataFrame('http://localhost:9200', 'flights')['Carrier'] >>> s.nunique() 4 """ @@ -1555,7 +1555,7 @@ class Series(NDFrame): Examples -------- - >>> s = ed.DataFrame('localhost', 'flights')['AvgTicketPrice'] + >>> s = ed.DataFrame('http://localhost:9200', 'flights')['AvgTicketPrice'] >>> int(s.var()) 70964 """ @@ -1577,7 +1577,7 @@ class Series(NDFrame): Examples -------- - >>> s = ed.DataFrame('localhost', 'flights')['AvgTicketPrice'] + >>> s = ed.DataFrame('http://localhost:9200', 'flights')['AvgTicketPrice'] >>> int(s.std()) 266 """ @@ -1599,7 +1599,7 @@ class Series(NDFrame): Examples -------- - >>> s = ed.DataFrame('localhost', 'flights')['AvgTicketPrice'] + >>> s = ed.DataFrame('http://localhost:9200', 'flights')['AvgTicketPrice'] >>> int(s.mad()) 213 """ @@ -1627,7 +1627,7 @@ class Series(NDFrame): Examples -------- - >>> df = ed.DataFrame('localhost', 'flights') # ignoring percentiles as they don't generate consistent results + >>> df = ed.DataFrame('http://localhost:9200', 'flights') # ignoring percentiles as they don't generate consistent results >>> df.AvgTicketPrice.describe() # doctest: +SKIP count 13059.000000 mean 628.253689 @@ -1660,7 +1660,7 @@ class Series(NDFrame): Examples -------- - >>> ed_s = ed.Series('localhost', 'flights', name='Carrier').head(5) + >>> ed_s = ed.Series('http://localhost:9200', 'flights', name='Carrier').head(5) >>> pd_s = ed.eland_to_pandas(ed_s) >>> print(f"type(ed_s)={type(ed_s)}\\ntype(pd_s)={type(pd_s)}") type(ed_s)= diff --git a/noxfile.py b/noxfile.py index 0a4f45c..4c1cec0 100644 --- a/noxfile.py +++ b/noxfile.py @@ -71,19 +71,19 @@ def lint(session): # Install numpy to use its mypy plugin # https://numpy.org/devdocs/reference/typing.html#mypy-plugin session.install("black", "flake8", "mypy", "isort", "numpy") - session.install("--pre", "elasticsearch") + session.install("--pre", "elasticsearch>=8.0.0a1,<9") session.run("python", "utils/license-headers.py", "check", *SOURCE_FILES) session.run("black", "--check", "--target-version=py37", *SOURCE_FILES) session.run("isort", "--check", "--profile=black", *SOURCE_FILES) session.run("flake8", "--ignore=E501,W503,E402,E712,E203", *SOURCE_FILES) # TODO: When all files are typed we can change this to .run("mypy", "--strict", "eland/") - session.log("mypy --strict eland/") + session.log("mypy --show-error-codes --strict eland/") for typed_file in TYPED_FILES: if not os.path.isfile(typed_file): session.error(f"The file {typed_file!r} couldn't be found") process = subprocess.run( - ["mypy", "--strict", typed_file], + ["mypy", "--show-error-codes", "--strict", typed_file], env=session.env, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, @@ -100,14 +100,15 @@ def lint(session): session.error("\n" + "\n".join(sorted(set(errors)))) -@nox.session(python=["3.7", "3.8", "3.9"]) +@nox.session(python=["3.7", "3.8", "3.9", "3.10"]) @nox.parametrize("pandas_version", ["1.2.0", "1.3.0"]) def test(session, pandas_version: str): session.install("-r", "requirements-dev.txt") session.install(".") session.run("python", "-m", "pip", "install", f"pandas~={pandas_version}") session.run("python", "-m", "tests.setup_tests") - session.run( + + pytest_args = ( "python", "-m", "pytest", @@ -116,6 +117,13 @@ def test(session, pandas_version: str): "--cov-config=setup.cfg", "--doctest-modules", "--nbval", + ) + + # PyTorch doesn't support Python 3.10 yet + if session.python == "3.10": + pytest_args += ("--ignore=eland/ml/pytorch",) + session.run( + *pytest_args, *(session.posargs or ("eland/", "tests/")), ) @@ -144,21 +152,25 @@ def docs(session): # See if we have an Elasticsearch cluster active # to rebuild the Jupyter notebooks with. + es_active = False try: - import elasticsearch + from elasticsearch import ConnectionError, Elasticsearch - es = elasticsearch.Elasticsearch("localhost:9200") - es.info() - if not es.indices.exists("flights"): - session.run("python", "-m", "tests.setup_tests") - es_active = True - except Exception: - es_active = False + try: + es = Elasticsearch("http://localhost:9200") + es.info() + if not es.indices.exists(index="flights"): + session.run("python", "-m", "tests.setup_tests") + es_active = True + except ConnectionError: + pass + except ImportError: + pass # Rebuild all the example notebooks inplace if es_active: session.install("jupyter-client", "ipykernel") - for filename in os.listdir(BASE_DIR / "docs/source/examples"): + for filename in os.listdir(BASE_DIR / "docs/sphinx/examples"): if ( filename.endswith(".ipynb") and filename != "introduction_to_eland_webinar.ipynb" @@ -170,7 +182,7 @@ def docs(session): "notebook", "--inplace", "--execute", - str(BASE_DIR / "docs/source/examples" / filename), + str(BASE_DIR / "docs/sphinx/examples" / filename), ) session.cd("docs") diff --git a/requirements-dev.txt b/requirements-dev.txt index a2c977c..3b0f186 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -1,4 +1,4 @@ -elasticsearch>=7.7 +elasticsearch>=8.0.0a1,<9 pandas>=1.2.0 matplotlib pytest>=5.2.1 @@ -11,6 +11,9 @@ nox lightgbm pytest-cov mypy -sentence-transformers>=2.1.0 -torch>=1.9.0 -transformers[torch]>=4.12.0 +huggingface-hub>=0.0.17 + +# Torch doesn't support Python 3.10 yet (pytorch/pytorch#66424) +sentence-transformers>=2.1.0; python_version<'3.10' +torch>=1.9.0; python_version<'3.10' +transformers[torch]>=4.12.0; python_version<'3.10' diff --git a/requirements.txt b/requirements.txt index 3b229a4..0dcf99f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,3 +1,3 @@ -elasticsearch>=7.7 +elasticsearch>=8.0.0a1,<9 pandas>=1 matplotlib diff --git a/setup.py b/setup.py index 4bcbe52..215bf46 100644 --- a/setup.py +++ b/setup.py @@ -82,7 +82,7 @@ setup( keywords="elastic eland pandas python", packages=find_packages(include=["eland", "eland.*"]), install_requires=[ - "elasticsearch>=7.11,<8", + "elasticsearch>=8.0.0a1,<9", "pandas>=1.2,<1.4", "matplotlib", "numpy", diff --git a/tests/__init__.py b/tests/__init__.py index 7b0ba96..a8d1e77 100644 --- a/tests/__init__.py +++ b/tests/__init__.py @@ -25,17 +25,12 @@ from eland.common import es_version ROOT_DIR = os.path.dirname(os.path.abspath(__file__)) # Define test files and indices -ELASTICSEARCH_HOST = os.environ.get("ELASTICSEARCH_HOST") or "localhost" +ELASTICSEARCH_HOST = os.environ.get( + "ELASTICSEARCH_URL", os.environ.get("ELASTICSEARCH_HOST", "http://localhost:9200") +) # Define client to use in tests -TEST_SUITE = os.environ.get("TEST_SUITE", "xpack") -if TEST_SUITE == "xpack": - ES_TEST_CLIENT = Elasticsearch( - ELASTICSEARCH_HOST, - http_auth=("elastic", "changeme"), - ) -else: - ES_TEST_CLIENT = Elasticsearch(ELASTICSEARCH_HOST) +ES_TEST_CLIENT = Elasticsearch(ELASTICSEARCH_HOST) ES_VERSION = es_version(ES_TEST_CLIENT) diff --git a/tests/dataframe/test_datetime_pytest.py b/tests/dataframe/test_datetime_pytest.py index 66a6cca..adc8d80 100644 --- a/tests/dataframe/test_datetime_pytest.py +++ b/tests/dataframe/test_datetime_pytest.py @@ -42,7 +42,7 @@ class TestDataFrameDateTime(TestData): usually contains tests). """ es = ES_TEST_CLIENT - if es.indices.exists(cls.time_index_name): + if es.indices.exists(index=cls.time_index_name): es.indices.delete(index=cls.time_index_name) dts = [datetime.strptime(time, "%Y-%m-%dT%H:%M:%S.%f%z") for time in cls.times] @@ -58,11 +58,11 @@ class TestDataFrameDateTime(TestData): body = {"mappings": mappings} index = "test_time_formats" - es.indices.delete(index=index, ignore=[400, 404]) + es.options(ignore_status=[400, 404]).indices.delete(index=index) es.indices.create(index=index, body=body) for i, time_formats in enumerate(time_formats_docs): - es.index(index=index, body=time_formats, id=i) + es.index(index=index, id=i, document=time_formats) es.indices.refresh(index=index) @classmethod diff --git a/tests/dataframe/test_query_pytest.py b/tests/dataframe/test_query_pytest.py index 7269295..44f4507 100644 --- a/tests/dataframe/test_query_pytest.py +++ b/tests/dataframe/test_query_pytest.py @@ -69,7 +69,7 @@ class TestDataFrameQuery(TestData): assert_pandas_eland_frame_equal(pd_q4, ed_q4) - ES_TEST_CLIENT.indices.delete(index_name) + ES_TEST_CLIENT.indices.delete(index=index_name) def test_simple_query(self): ed_flights = self.ed_flights() @@ -141,4 +141,4 @@ class TestDataFrameQuery(TestData): assert_pandas_eland_frame_equal(pd_q4, ed_q4) - ES_TEST_CLIENT.indices.delete(index_name) + ES_TEST_CLIENT.indices.delete(index=index_name) diff --git a/tests/dataframe/test_to_csv_pytest.py b/tests/dataframe/test_to_csv_pytest.py index b17d1de..49c05c7 100644 --- a/tests/dataframe/test_to_csv_pytest.py +++ b/tests/dataframe/test_to_csv_pytest.py @@ -99,7 +99,7 @@ class TestDataFrameToCSV(TestData): print(pd_flights_from_csv.head()) # clean up index - ES_TEST_CLIENT.indices.delete(test_index) + ES_TEST_CLIENT.indices.delete(index=test_index) def test_pd_to_csv_without_filepath(self): diff --git a/tests/dataframe/test_utils_pytest.py b/tests/dataframe/test_utils_pytest.py index d76333c..bc6b4ea 100644 --- a/tests/dataframe/test_utils_pytest.py +++ b/tests/dataframe/test_utils_pytest.py @@ -122,7 +122,7 @@ class TestDataFrameUtils(TestData): } } - mapping = ES_TEST_CLIENT.indices.get_mapping(index_name) + mapping = ES_TEST_CLIENT.indices.get_mapping(index=index_name) assert expected_mapping == mapping diff --git a/tests/etl/test_pandas_to_eland.py b/tests/etl/test_pandas_to_eland.py index c99f57c..72a6738 100644 --- a/tests/etl/test_pandas_to_eland.py +++ b/tests/etl/test_pandas_to_eland.py @@ -195,7 +195,7 @@ class TestPandasToEland: ) # Assert that the value 128 caused the index error - assert "Value [128] is out of range for a byte" in str(e.value) + assert "Value [128] is out of range for a byte" in str(e.value.errors) def test_pandas_to_eland_text_inserts_keyword(self): es = ES_TEST_CLIENT diff --git a/tests/field_mappings/test_datetime_pytest.py b/tests/field_mappings/test_datetime_pytest.py index 77364dc..27ae00c 100644 --- a/tests/field_mappings/test_datetime_pytest.py +++ b/tests/field_mappings/test_datetime_pytest.py @@ -32,7 +32,7 @@ class TestDateTime(TestData): usually contains tests). """ es = ES_TEST_CLIENT - if es.indices.exists(cls.time_index_name): + if es.indices.exists(index=cls.time_index_name): es.indices.delete(index=cls.time_index_name) dts = [datetime.strptime(time, "%Y-%m-%dT%H:%M:%S.%f%z") for time in cls.times] @@ -46,13 +46,12 @@ class TestDateTime(TestData): mappings["properties"][field_name]["type"] = "date" mappings["properties"][field_name]["format"] = field_name - body = {"mappings": mappings} index = "test_time_formats" - es.indices.delete(index=index, ignore=[400, 404]) - es.indices.create(index=index, body=body) + es.options(ignore_status=[400, 404]).indices.delete(index=index) + es.indices.create(index=index, mappings=mappings) for i, time_formats in enumerate(time_formats_docs): - es.index(index=index, body=time_formats, id=i) + es.index(index=index, id=i, document=time_formats) es.indices.refresh(index=index) @classmethod diff --git a/tests/ml/pytorch/test_pytorch_model_pytest.py b/tests/ml/pytorch/test_pytorch_model_pytest.py index 17c6820..39676e5 100644 --- a/tests/ml/pytorch/test_pytorch_model_pytest.py +++ b/tests/ml/pytorch/test_pytorch_model_pytest.py @@ -90,5 +90,5 @@ class TestPytorchModel: def test_text_classification(self, model_id, task, text_input, value): with tempfile.TemporaryDirectory() as tmp_dir: ptm = download_model_and_start_deployment(tmp_dir, True, model_id, task) - result = ptm.infer({"docs": [{"text_field": text_input}]}) + result = ptm.infer(docs=[{"text_field": text_input}]) assert result["predicted_value"] == value diff --git a/tests/notebook/test_demo_notebook.ipynb b/tests/notebook/test_demo_notebook.ipynb index 1e6dcdc..f387d29 100644 --- a/tests/notebook/test_demo_notebook.ipynb +++ b/tests/notebook/test_demo_notebook.ipynb @@ -49,7 +49,7 @@ "metadata": {}, "outputs": [], "source": [ - "ed_flights = ed.DataFrame('localhost', 'flights')" + "ed_flights = ed.DataFrame('http://localhost:9200', 'flights')" ] }, { @@ -59,7 +59,9 @@ "outputs": [ { "data": { - "text/plain": "eland.dataframe.DataFrame" + "text/plain": [ + "eland.dataframe.DataFrame" + ] }, "execution_count": 3, "metadata": {}, @@ -93,7 +95,9 @@ "outputs": [ { "data": { - "text/plain": "pandas.core.frame.DataFrame" + "text/plain": [ + "pandas.core.frame.DataFrame" + ] }, "execution_count": 5, "metadata": {}, @@ -125,7 +129,15 @@ "outputs": [ { "data": { - "text/plain": "Index(['AvgTicketPrice', 'Cancelled', 'Carrier', 'Dest', 'DestAirportID', 'DestCityName',\n 'DestCountry', 'DestLocation', 'DestRegion', 'DestWeather', 'DistanceKilometers',\n 'DistanceMiles', 'FlightDelay', 'FlightDelayMin', 'FlightDelayType', 'FlightNum',\n 'FlightTimeHour', 'FlightTimeMin', 'Origin', 'OriginAirportID', 'OriginCityName',\n 'OriginCountry', 'OriginLocation', 'OriginRegion', 'OriginWeather', 'dayOfWeek',\n 'timestamp'],\n dtype='object')" + "text/plain": [ + "Index(['AvgTicketPrice', 'Cancelled', 'Carrier', 'Dest', 'DestAirportID', 'DestCityName',\n", + " 'DestCountry', 'DestLocation', 'DestRegion', 'DestWeather', 'DistanceKilometers',\n", + " 'DistanceMiles', 'FlightDelay', 'FlightDelayMin', 'FlightDelayType', 'FlightNum',\n", + " 'FlightTimeHour', 'FlightTimeMin', 'Origin', 'OriginAirportID', 'OriginCityName',\n", + " 'OriginCountry', 'OriginLocation', 'OriginRegion', 'OriginWeather', 'dayOfWeek',\n", + " 'timestamp'],\n", + " dtype='object')" + ] }, "execution_count": 6, "metadata": {}, @@ -143,7 +155,15 @@ "outputs": [ { "data": { - "text/plain": "Index(['AvgTicketPrice', 'Cancelled', 'Carrier', 'Dest', 'DestAirportID', 'DestCityName',\n 'DestCountry', 'DestLocation', 'DestRegion', 'DestWeather', 'DistanceKilometers',\n 'DistanceMiles', 'FlightDelay', 'FlightDelayMin', 'FlightDelayType', 'FlightNum',\n 'FlightTimeHour', 'FlightTimeMin', 'Origin', 'OriginAirportID', 'OriginCityName',\n 'OriginCountry', 'OriginLocation', 'OriginRegion', 'OriginWeather', 'dayOfWeek',\n 'timestamp'],\n dtype='object')" + "text/plain": [ + "Index(['AvgTicketPrice', 'Cancelled', 'Carrier', 'Dest', 'DestAirportID', 'DestCityName',\n", + " 'DestCountry', 'DestLocation', 'DestRegion', 'DestWeather', 'DistanceKilometers',\n", + " 'DistanceMiles', 'FlightDelay', 'FlightDelayMin', 'FlightDelayType', 'FlightNum',\n", + " 'FlightTimeHour', 'FlightTimeMin', 'Origin', 'OriginAirportID', 'OriginCityName',\n", + " 'OriginCountry', 'OriginLocation', 'OriginRegion', 'OriginWeather', 'dayOfWeek',\n", + " 'timestamp'],\n", + " dtype='object')" + ] }, "execution_count": 7, "metadata": {}, @@ -168,7 +188,20 @@ "outputs": [ { "data": { - 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AvgTicketPriceDistanceKilometers...FlightTimeMindayOfWeek
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AvgTicketPriceCancelled...dayOfWeektimestamp
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AvgTicketPriceCancelled...dayOfWeektimestamp
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AvgTicketPriceCancelled...dayOfWeektimestamp
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AvgTicketPriceCancelled...dayOfWeektimestamp
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" + ], + "text/plain": [ + " AvgTicketPrice Cancelled ... dayOfWeek timestamp\n", + "13054 1080.446279 False ... 6 2018-02-11 20:42:25\n", + "13055 646.612941 False ... 6 2018-02-11 01:41:57\n", + "13056 997.751876 False ... 6 2018-02-11 04:09:27\n", + "13057 1102.814465 False ... 6 2018-02-11 08:28:21\n", + "13058 858.144337 False ... 6 2018-02-11 14:54:34\n", + "\n", + "[5 rows x 27 columns]" + ] }, "execution_count": 24, "metadata": {}, @@ -550,7 +1215,15 @@ "outputs": [ { "data": { - "text/plain": "Index(['AvgTicketPrice', 'Cancelled', 'Carrier', 'Dest', 'DestAirportID', 'DestCityName',\n 'DestCountry', 'DestLocation', 'DestRegion', 'DestWeather', 'DistanceKilometers',\n 'DistanceMiles', 'FlightDelay', 'FlightDelayMin', 'FlightDelayType', 'FlightNum',\n 'FlightTimeHour', 'FlightTimeMin', 'Origin', 'OriginAirportID', 'OriginCityName',\n 'OriginCountry', 'OriginLocation', 'OriginRegion', 'OriginWeather', 'dayOfWeek',\n 'timestamp'],\n dtype='object')" + "text/plain": [ + "Index(['AvgTicketPrice', 'Cancelled', 'Carrier', 'Dest', 'DestAirportID', 'DestCityName',\n", + " 'DestCountry', 'DestLocation', 'DestRegion', 'DestWeather', 'DistanceKilometers',\n", + " 'DistanceMiles', 'FlightDelay', 'FlightDelayMin', 'FlightDelayType', 'FlightNum',\n", + " 'FlightTimeHour', 'FlightTimeMin', 'Origin', 'OriginAirportID', 'OriginCityName',\n", + " 'OriginCountry', 'OriginLocation', 'OriginRegion', 'OriginWeather', 'dayOfWeek',\n", + " 'timestamp'],\n", + " dtype='object')" + ] }, "execution_count": 25, "metadata": {}, @@ -568,7 +1241,15 @@ "outputs": [ { "data": { - "text/plain": "Index(['AvgTicketPrice', 'Cancelled', 'Carrier', 'Dest', 'DestAirportID', 'DestCityName',\n 'DestCountry', 'DestLocation', 'DestRegion', 'DestWeather', 'DistanceKilometers',\n 'DistanceMiles', 'FlightDelay', 'FlightDelayMin', 'FlightDelayType', 'FlightNum',\n 'FlightTimeHour', 'FlightTimeMin', 'Origin', 'OriginAirportID', 'OriginCityName',\n 'OriginCountry', 'OriginLocation', 'OriginRegion', 'OriginWeather', 'dayOfWeek',\n 'timestamp'],\n dtype='object')" + "text/plain": [ + "Index(['AvgTicketPrice', 'Cancelled', 'Carrier', 'Dest', 'DestAirportID', 'DestCityName',\n", + " 'DestCountry', 'DestLocation', 'DestRegion', 'DestWeather', 'DistanceKilometers',\n", + " 'DistanceMiles', 'FlightDelay', 'FlightDelayMin', 'FlightDelayType', 'FlightNum',\n", + " 'FlightTimeHour', 'FlightTimeMin', 'Origin', 'OriginAirportID', 'OriginCityName',\n", + " 'OriginCountry', 'OriginLocation', 'OriginRegion', 'OriginWeather', 'dayOfWeek',\n", + " 'timestamp'],\n", + " dtype='object')" + ] }, "execution_count": 26, "metadata": {}, @@ -593,7 +1274,20 @@ "outputs": [ { "data": { - "text/plain": "0 Kibana Airlines\n1 Logstash Airways\n2 Logstash Airways\n3 Kibana Airlines\n4 Kibana Airlines\n ... \n13054 Logstash Airways\n13055 Logstash Airways\n13056 Logstash Airways\n13057 JetBeats\n13058 JetBeats\nName: Carrier, Length: 13059, dtype: object" + "text/plain": [ + "0 Kibana Airlines\n", + "1 Logstash Airways\n", + "2 Logstash Airways\n", + "3 Kibana Airlines\n", + "4 Kibana Airlines\n", + " ... \n", + "13054 Logstash Airways\n", + "13055 Logstash Airways\n", + "13056 Logstash Airways\n", + "13057 JetBeats\n", + "13058 JetBeats\n", + "Name: Carrier, Length: 13059, dtype: object" + ] }, "execution_count": 27, "metadata": {}, @@ -611,7 +1305,20 @@ "outputs": [ { "data": { - "text/plain": "0 Kibana Airlines\n1 Logstash Airways\n2 Logstash Airways\n3 Kibana Airlines\n4 Kibana Airlines\n ... \n13054 Logstash Airways\n13055 Logstash Airways\n13056 Logstash Airways\n13057 JetBeats\n13058 JetBeats\nName: Carrier, Length: 13059, dtype: object" + "text/plain": [ + "0 Kibana Airlines\n", + "1 Logstash Airways\n", + "2 Logstash Airways\n", + "3 Kibana Airlines\n", + "4 Kibana Airlines\n", + " ... \n", + "13054 Logstash Airways\n", + "13055 Logstash Airways\n", + "13056 Logstash Airways\n", + "13057 JetBeats\n", + "13058 JetBeats\n", + "Name: Carrier, Length: 13059, dtype: object" + ] }, "execution_count": 28, "metadata": {}, @@ -629,8 +1336,106 @@ "outputs": [ { "data": { - "text/plain": " Carrier Origin\n0 Kibana Airlines Frankfurt am Main Airport\n1 Logstash Airways Cape Town International Airport\n2 Logstash Airways Venice Marco Polo Airport\n3 Kibana Airlines Naples International Airport\n4 Kibana Airlines Licenciado Benito Juarez International Airport\n... ... ...\n13054 Logstash Airways Pisa International Airport\n13055 Logstash Airways Winnipeg / James Armstrong Richardson Internat...\n13056 Logstash Airways Licenciado Benito Juarez International Airport\n13057 JetBeats Itami Airport\n13058 JetBeats Adelaide International Airport\n\n[13059 rows x 2 columns]", - "text/html": "
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CarrierOrigin
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1Logstash AirwaysCape Town International Airport
2Logstash AirwaysVenice Marco Polo Airport
3Kibana AirlinesNaples International Airport
4Kibana AirlinesLicenciado Benito Juarez International Airport
.........
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13058JetBeatsAdelaide International Airport
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AvgTicketPriceCancelled...dayOfWeektimestamp
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AvgTicketPriceCancelled...dayOfWeektimestamp
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AvgTicketPriceCancelled...dayOfWeektimestamp
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AvgTicketPriceCancelled...dayOfWeektimestamp
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AvgTicketPriceCancelled...dayOfWeektimestamp
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AvgTicketPriceCancelled...dayOfWeektimestamp
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311946.358410True...02018-01-01 11:51:12
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AvgTicketPriceCancelled...dayOfWeektimestamp
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DistanceKilometersAvgTicketPrice
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DistanceKilometersAvgTicketPrice
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DistanceKilometersAvgTicketPrice
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AvgTicketPriceDistanceKilometers...FlightTimeMindayOfWeek
count13059.00000013059.000000...13059.00000013059.000000
mean628.2536897092.142455...511.1278422.835975
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AvgTicketPriceDistanceKilometers...FlightTimeMindayOfWeek
count13059.00000013059.000000...13059.00000013059.000000
mean628.2536897092.142457...511.1278422.835975
std266.3866614578.263193...334.7411351.939365
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AvgTicketPriceDistanceKilometers...FlightTimeMindayOfWeek
count13059.00000013059.000000...13059.00000013059.000000
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std266.3866614578.263193...334.7411351.939365
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FlightTimeMin dayOfWeek\n", + "count 13059.000000 13059.000000 ... 13059.000000 13059.000000\n", + "mean 628.253689 7092.142457 ... 511.127842 2.835975\n", + "std 266.386661 4578.263193 ... 334.741135 1.939365\n", + "min 100.020531 0.000000 ... 0.000000 0.000000\n", + "25% 410.008918 2470.545974 ... 251.938710 1.000000\n", + "50% 640.387285 7612.072403 ... 503.148975 3.000000\n", + "75% 842.213490 9735.660463 ... 720.505705 4.000000\n", + "max 1199.729004 19881.482422 ... 1902.901978 6.000000\n", + "\n", + "[8 rows x 7 columns]" + ] }, "execution_count": 40, "metadata": {}, @@ -1063,7 +2738,18 @@ "outputs": [ { "data": { - "text/plain": "AvgTicketPrice 1199.729053\nCancelled 1.000000\nDistanceKilometers 19881.482315\nDistanceMiles 12353.780369\nFlightDelay 1.000000\nFlightDelayMin 360.000000\nFlightTimeHour 31.715034\nFlightTimeMin 1902.902032\ndayOfWeek 6.000000\ndtype: float64" + "text/plain": [ + "AvgTicketPrice 1199.729053\n", + "Cancelled 1.000000\n", + "DistanceKilometers 19881.482315\n", + "DistanceMiles 12353.780369\n", + "FlightDelay 1.000000\n", + "FlightDelayMin 360.000000\n", + "FlightTimeHour 31.715034\n", + "FlightTimeMin 1902.902032\n", + "dayOfWeek 6.000000\n", + "dtype: float64" + ] }, "execution_count": 43, "metadata": {}, @@ -1088,7 +2774,18 @@ "outputs": [ { "data": { - "text/plain": "AvgTicketPrice 1199.729004\nCancelled 1.000000\nDistanceKilometers 19881.482422\nDistanceMiles 12353.780273\nFlightDelay 1.000000\nFlightDelayMin 360.000000\nFlightTimeHour 31.715034\nFlightTimeMin 1902.901978\ndayOfWeek 6.000000\ndtype: float64" + "text/plain": [ + "AvgTicketPrice 1199.729004\n", + "Cancelled 1.000000\n", + "DistanceKilometers 19881.482422\n", + "DistanceMiles 12353.780273\n", + "FlightDelay 1.000000\n", + "FlightDelayMin 360.000000\n", + "FlightTimeHour 31.715034\n", + "FlightTimeMin 1902.901978\n", + "dayOfWeek 6.000000\n", + "dtype: float64" + ] }, "execution_count": 44, "metadata": {}, @@ -1113,7 +2810,18 @@ "outputs": [ { "data": { - "text/plain": "AvgTicketPrice 100.020528\nCancelled 0.000000\nDistanceKilometers 0.000000\nDistanceMiles 0.000000\nFlightDelay 0.000000\nFlightDelayMin 0.000000\nFlightTimeHour 0.000000\nFlightTimeMin 0.000000\ndayOfWeek 0.000000\ndtype: float64" + "text/plain": [ + "AvgTicketPrice 100.020528\n", + "Cancelled 0.000000\n", + "DistanceKilometers 0.000000\n", + "DistanceMiles 0.000000\n", + "FlightDelay 0.000000\n", + "FlightDelayMin 0.000000\n", + "FlightTimeHour 0.000000\n", + "FlightTimeMin 0.000000\n", + "dayOfWeek 0.000000\n", + "dtype: float64" + ] }, "execution_count": 45, "metadata": {}, @@ -1131,7 +2839,18 @@ "outputs": [ { "data": { - "text/plain": "AvgTicketPrice 100.020531\nCancelled 0.000000\nDistanceKilometers 0.000000\nDistanceMiles 0.000000\nFlightDelay 0.000000\nFlightDelayMin 0.000000\nFlightTimeHour 0.000000\nFlightTimeMin 0.000000\ndayOfWeek 0.000000\ndtype: float64" + "text/plain": [ + "AvgTicketPrice 100.020531\n", + "Cancelled 0.000000\n", + "DistanceKilometers 0.000000\n", + "DistanceMiles 0.000000\n", + "FlightDelay 0.000000\n", + "FlightDelayMin 0.000000\n", + "FlightTimeHour 0.000000\n", + "FlightTimeMin 0.000000\n", + "dayOfWeek 0.000000\n", + "dtype: float64" + ] }, "execution_count": 46, "metadata": {}, @@ -1156,7 +2875,18 @@ "outputs": [ { "data": { - "text/plain": "AvgTicketPrice 628.253689\nCancelled 0.128494\nDistanceKilometers 7092.142455\nDistanceMiles 4406.853013\nFlightDelay 0.251168\nFlightDelayMin 47.335171\nFlightTimeHour 8.518797\nFlightTimeMin 511.127842\ndayOfWeek 2.835975\ndtype: float64" + "text/plain": [ + "AvgTicketPrice 628.253689\n", + "Cancelled 0.128494\n", + "DistanceKilometers 7092.142455\n", + "DistanceMiles 4406.853013\n", + "FlightDelay 0.251168\n", + "FlightDelayMin 47.335171\n", + "FlightTimeHour 8.518797\n", + "FlightTimeMin 511.127842\n", + "dayOfWeek 2.835975\n", + "dtype: float64" + ] }, "execution_count": 47, "metadata": {}, @@ -1174,7 +2904,18 @@ "outputs": [ { "data": { - "text/plain": "AvgTicketPrice 628.253689\nCancelled 0.128494\nDistanceKilometers 7092.142457\nDistanceMiles 4406.853010\nFlightDelay 0.251168\nFlightDelayMin 47.335171\nFlightTimeHour 8.518797\nFlightTimeMin 511.127842\ndayOfWeek 2.835975\ndtype: float64" + "text/plain": [ + "AvgTicketPrice 628.253689\n", + "Cancelled 0.128494\n", + "DistanceKilometers 7092.142457\n", + "DistanceMiles 4406.853010\n", + "FlightDelay 0.251168\n", + "FlightDelayMin 47.335171\n", + "FlightTimeHour 8.518797\n", + "FlightTimeMin 511.127842\n", + "dayOfWeek 2.835975\n", + "dtype: float64" + ] }, "execution_count": 48, "metadata": {}, @@ -1199,7 +2940,18 @@ "outputs": [ { "data": { - "text/plain": "AvgTicketPrice 8.204365e+06\nCancelled 1.678000e+03\nDistanceKilometers 9.261629e+07\nDistanceMiles 5.754909e+07\nFlightDelay 3.280000e+03\nFlightDelayMin 6.181500e+05\nFlightTimeHour 1.112470e+05\nFlightTimeMin 6.674818e+06\ndayOfWeek 3.703500e+04\ndtype: float64" + "text/plain": [ + "AvgTicketPrice 8.204365e+06\n", + "Cancelled 1.678000e+03\n", + "DistanceKilometers 9.261629e+07\n", + "DistanceMiles 5.754909e+07\n", + "FlightDelay 3.280000e+03\n", + "FlightDelayMin 6.181500e+05\n", + "FlightTimeHour 1.112470e+05\n", + "FlightTimeMin 6.674818e+06\n", + "dayOfWeek 3.703500e+04\n", + "dtype: float64" + ] }, "execution_count": 49, "metadata": {}, @@ -1217,7 +2969,18 @@ "outputs": [ { "data": { - "text/plain": "AvgTicketPrice 8.204365e+06\nCancelled 1.678000e+03\nDistanceKilometers 9.261629e+07\nDistanceMiles 5.754909e+07\nFlightDelay 3.280000e+03\nFlightDelayMin 6.181500e+05\nFlightTimeHour 1.112470e+05\nFlightTimeMin 6.674818e+06\ndayOfWeek 3.703500e+04\ndtype: float64" + "text/plain": [ + "AvgTicketPrice 8.204365e+06\n", + "Cancelled 1.678000e+03\n", + "DistanceKilometers 9.261629e+07\n", + "DistanceMiles 5.754909e+07\n", + "FlightDelay 3.280000e+03\n", + "FlightDelayMin 6.181500e+05\n", + "FlightTimeHour 1.112470e+05\n", + "FlightTimeMin 6.674818e+06\n", + "dayOfWeek 3.703500e+04\n", + "dtype: float64" + ] }, "execution_count": 50, "metadata": {}, @@ -1242,7 +3005,12 @@ "outputs": [ { "data": { - "text/plain": "Carrier 4\nOrigin 156\nDest 156\ndtype: int64" + "text/plain": [ + "Carrier 4\n", + "Origin 156\n", + "Dest 156\n", + "dtype: int64" + ] }, "execution_count": 51, "metadata": {}, @@ -1260,7 +3028,12 @@ "outputs": [ { "data": { - "text/plain": "Carrier 4\nOrigin 156\nDest 156\ndtype: int64" + "text/plain": [ + "Carrier 4\n", + "Origin 156\n", + "Dest 156\n", + "dtype: int64" + ] }, "execution_count": 52, "metadata": {}, @@ -1285,8 +3058,142 @@ "outputs": [ { "data": { - 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CarrierDestRegion...dayOfWeektimestamp
0Kibana AirlinesSE-BD...02018-01-01 00:00:00
1Logstash AirwaysIT-34...02018-01-01 18:27:00
2Logstash AirwaysIT-34...02018-01-01 17:11:14
3Kibana AirlinesIT-34...02018-01-01 10:33:28
4Kibana AirlinesSE-BD...02018-01-01 05:13:00
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13054Logstash AirwaysSE-BD...62018-02-11 20:42:25
13055Logstash AirwaysCH-ZH...62018-02-11 01:41:57
13056Logstash AirwaysRU-AMU...62018-02-11 04:09:27
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CarrierDestRegion...dayOfWeektimestamp
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3Kibana AirlinesIT-34...02018-01-01 10:33:28
4Kibana AirlinesSE-BD...02018-01-01 05:13:00
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13054Logstash AirwaysSE-BD...62018-02-11 20:42:25
13055Logstash AirwaysCH-ZH...62018-02-11 01:41:57
13056Logstash AirwaysRU-AMU...62018-02-11 04:09:27
13057JetBeatsSE-BD...62018-02-11 08:28:21
13058JetBeatsUS-DC...62018-02-11 14:54:34
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CarrierDestRegion...dayOfWeektimestamp
0Kibana AirlinesSE-BD...02018-01-01 00:00:00
1Logstash AirwaysIT-34...02018-01-01 18:27:00
2Logstash AirwaysIT-34...02018-01-01 17:11:14
3Kibana AirlinesIT-34...02018-01-01 10:33:28
4Kibana AirlinesSE-BD...02018-01-01 05:13:00
..................
13054Logstash AirwaysSE-BD...62018-02-11 20:42:25
13055Logstash AirwaysCH-ZH...62018-02-11 01:41:57
13056Logstash AirwaysRU-AMU...62018-02-11 04:09:27
13057JetBeatsSE-BD...62018-02-11 08:28:21
13058JetBeatsUS-DC...62018-02-11 14:54:34
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CarrierDestRegion...dayOfWeektimestamp
0Kibana AirlinesSE-BD...02018-01-01 00:00:00
1Logstash AirwaysIT-34...02018-01-01 18:27:00
2Logstash AirwaysIT-34...02018-01-01 17:11:14
3Kibana AirlinesIT-34...02018-01-01 10:33:28
4Kibana AirlinesSE-BD...02018-01-01 05:13:00
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13054Logstash AirwaysSE-BD...62018-02-11 20:42:25
13055Logstash AirwaysCH-ZH...62018-02-11 01:41:57
13056Logstash AirwaysRU-AMU...62018-02-11 04:09:27
13057JetBeatsSE-BD...62018-02-11 08:28:21
13058JetBeatsUS-DC...62018-02-11 14:54:34
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" + ], + "text/plain": [ + " Carrier DestRegion ... dayOfWeek timestamp\n", + "0 Kibana Airlines SE-BD ... 0 2018-01-01 00:00:00\n", + "1 Logstash Airways IT-34 ... 0 2018-01-01 18:27:00\n", + "2 Logstash Airways IT-34 ... 0 2018-01-01 17:11:14\n", + "3 Kibana Airlines IT-34 ... 0 2018-01-01 10:33:28\n", + "4 Kibana Airlines SE-BD ... 0 2018-01-01 05:13:00\n", + "... ... ... ... ... ...\n", + "13054 Logstash Airways SE-BD ... 6 2018-02-11 20:42:25\n", + "13055 Logstash Airways CH-ZH ... 6 2018-02-11 01:41:57\n", + "13056 Logstash Airways RU-AMU ... 6 2018-02-11 04:09:27\n", + "13057 JetBeats SE-BD ... 6 2018-02-11 08:28:21\n", + "13058 JetBeats US-DC ... 6 2018-02-11 14:54:34\n", + "\n", + "[13059 rows x 20 columns]" + ] }, "execution_count": 54, "metadata": {}, @@ -1342,8 +3383,10 @@ "outputs": [ { "data": { - "text/plain": "
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\n" 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", 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zxuoH5kbE3ZL2AlZJWpnGXRQR/5KfWNLBwMnAIcD+wLclvTaNvgw4BtgI3ClpeUTc35S1MGsc54h1tJoLMUkLgXcBWyPiDantXOAvgJ+nyT4RESvSuI8DZwLPAn8TETem9pnAxcA44IsRMb/WmFrNSE45nju9n9N9anLLiojNwOY0/KSkB4DJQ7xlFrAkIp4BHpLUBxyRxvVFxDoASUvStP6RsZbmHLFON5Y9YlcBnwOuHtTuLRizCiRNBQ4DbgfeApwt6TTgLrI9AtvJfoBuy71tI8//KG0Y1H5khWXMAeYAdHV10dvbWzGWrj2yIn+0qs1vrHbu3Flx3rXE2KhYilCmWKDx8TQjR9JyWjJPqhlLntSyjmX6To5E2fJosJoLsYi4NSXNSHgLxjqapD2Ba4GPRsQTki4HzgMiPV8AfHCsy4mIBcACgO7u7ujp6ak43aWLl3HB6tGn//pTK89vrHp7e6kUaxF7g6+aOb5iLEWo9rkUpZHxNCtHoHXzpJqx5Mnc6f2jXsdmr99YlS2PBmvEMWIN2YKBkW/F1FMjK+mRbIXUukVWL0UsP/95F70ls3PnzjHPQ9KuZD8wiyPiGwARsSU3/grg+vRyEzAl9/YDUhtDtJu1NOeIdbJ6F2IN24KBkW/F1FMjK+mRbMXUsrVST0UsP7+1VfSWzFiLQEkCrgQeiIgLc+2T0rExAO8B7kvDy4GvSLqQrBt/GnAHIGCapIPIflxOBt4/puDMSsA5Yp2urr+w3oIxe5G3AB8AVku6J7V9AjhF0qFkGy3rgQ8BRMQaSUvJuuf7gbMi4lkASWcDN5Kd2LIwItY0bzXMGsY5Yh2troWYt2DMXigivkf2PR9sxRDvOR84v0L7iqHeZ9aKnCPW6cZy+YqvAj3AvpI2AucAPd6CMTMzMxuZsZw1eUqF5iuHmN5bMGZmZmY5vrJ+HYzkwqxmZmZmg/lek2ZmZmYFcSFmZmZmVhAXYmZmZmYFcSFmZmZmVhAXYmZmZmYFcSFmZmZmVhAXYmZmZmYFcSFmZmZmVhAXYmZmZmYFcSFmZmZmVhAXYmZmZmYFcSFmZmZmVhAXYmYNJGmKpFsk3S9pjaSPpPZ9JK2UtDY9T0ztknSJpD5J90o6PDev2Wn6tZJmF7VOZvXkHLFOt0vRAZTJ1Hk3vKht7vR+Tq/QbjZC/cDciLhb0l7AKkkrgdOBmyNivqR5wDzgY8BxwLT0OBK4HDhS0j7AOUA3EGk+yyNie9PXyKy+nCPW0bxHzKyBImJzRNydhp8EHgAmA7OARWmyRcCJaXgWcHVkbgMmSJoEHAusjIht6YdlJTCzeWti1hjOEet03iNm1iSSpgKHAbcDXRGxOY16BOhKw5OBDbm3bUxt1doHL2MOMAegq6uL3t7eirF07ZHt7R2tavMbq507d1acdy0xNiqWIpQpFmh8PM3IkbSclsyTasaSJ7WsY5m+kyNRtjwazIWYWRNI2hO4FvhoRDwh6blxERGSoh7LiYgFwAKA7u7u6OnpqTjdpYuXccHq0af/+lMrz2+sent7qRRrEYcFXDVzfMVYilDtcylKI+NpVo6k+bVknlQzljyZO71/1OvY7PUbq7Ll0WDumjRrMEm7kv3ALI6Ib6TmLak7hfS8NbVvAqbk3n5AaqvWbtbynCPWyVyImTWQss36K4EHIuLC3KjlwMBZXbOBZbn209KZYUcBO1L3zI3ADEkT09ljM1KbWUtzjlinc9ekWWO9BfgAsFrSPantE8B8YKmkM4GHgZPSuBXA8UAf8DRwBkBEbJN0HnBnmu7TEbGtKWtg1ljOEetoLsTMGigivgeoyuijK0wfwFlV5rUQWFi/6MyK5xyxTueuSTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK8iYCjFJCyVtlXRfrm0fSSslrU3PE1O7JF0iqU/SvZIOz71ndpp+raTZlZZlZmZm1m7GukfsKmDmoLZ5wM0RMQ24Ob0GOA6Ylh5zgMshK9yAc4AjgSOAcwaKNzMzM7N2NqZCLCJuBQbfy2sWsCgNLwJOzLVfHZnbgAmSJgHHAisjYltEbAdW8uLizszMzKztNOJek10RsTkNPwJ0peHJwIbcdBtTW7X2F5E0h2xvGl1dXfT29tYvamDu9P4XtXXtUbm9WTpx+fm/686dO+v+dx6NnTt3FrZsMzNrfw296XdEhKSo4/wWAAsAuru7o6enp16zBuD0eTe8qG3u9H4uWF3cvdE7cfnrT+15bri3t5d6/51Ho8gi0MzM2l8jzprckrocSc9bU/smYEpuugNSW7V2MzMzs7bWiEJsOTBw5uNsYFmu/bR09uRRwI7UhXkjMEPSxHSQ/ozUZmZmZtbWxnr5iq8C3wdeJ2mjpDOB+cAxktYC70yvAVYA64A+4ArgrwAiYhtwHnBnenw6tZm1vCqXeDlX0iZJ96TH8blxH0+XeHlQ0rG59pmprU/SvMHLMWtVzhHrdGM6+CciTqky6ugK0wZwVpX5LAQWjiUWs5K6CvgccPWg9osi4l/yDZIOBk4GDgH2B74t6bVp9GXAMWQns9wpaXlE3N/IwM2a5CqcI9bBijsK3KwDRMStkqaOcPJZwJKIeAZ4SFIf2bX1APoiYh2ApCVpWv/IWMtzjlincyFmVoyzJZ0G3AXMTdfQmwzclpsmfymXwZd4ObLSTEd6iZdaL0vSqLNIq12mpIhLtxR9yZS8MsUCTY+nITkCrZsn1YwlT2pZxzJ9J0eibHk0mAsxs+a7nOy4yEjPFwAfrMeMR3qJl0sXL6vpsiT5S4vUU7XLlFS6pEyjXTVzfKGXTMkr+vItgzUxnoblCLRunlQzljyp5RJFzV6/sSpbHg3mQsysySJiy8CwpCuA69PLoS7l4ku8WMdwjlgnacTlK8xsCAPX2UveAwycLbYcOFnS7pIOIrsv6x1kZxNPk3SQpN3IDlZe3syYzZrJOWKdxHvEzBooXeKlB9hX0kayG9z3SDqUrNtlPfAhgIhYI2kp2QHG/cBZEfFsms/ZZNfXGwcsjIg1zV0Ts8ZwjlincyFm1kBVLvFy5RDTnw+cX6F9Bdm1+MzainPEOp27Js3MzMwK0pZ7xKYWcKaVmZmZ2Wh5j5iZmZlZQdpyj5iZWa1Wb9pR03WZ1s8/oQHRmJVPrb1OzpHKvEfMzMzMrCAuxMzMzMwK4kLMzMzMrCAuxMzMzMwK4kLMzMzMrCAuxMzMzMwK4stXWOnkT42eO71/xJcS8KnRZmbWarxHzMzMzKwgLsTMzMzMCuJCzMzMzKwgLsTMzMzMCuJCzMzMzKwgLsTMGkjSQklbJd2Xa9tH0kpJa9PzxNQuSZdI6pN0r6TDc++ZnaZfK2l2Eeti1ijOE+tkLsTMGusqYOagtnnAzRExDbg5vQY4DpiWHnOAyyH7QQLOAY4EjgDOGfhRMmsTV+E8sQ7lQsysgSLiVmDboOZZwKI0vAg4Mdd+dWRuAyZImgQcC6yMiG0RsR1YyYt/tMxalvPEOpkv6GrWfF0RsTkNPwJ0peHJwIbcdBtTW7X2F5E0h2wvAV1dXfT29lYOYI/sYrmjVW1+Y7Vz586K864lxrEq02dT7XMpSpPjcZ6M0FjypNZ1rEVR3+Wy5dFgLsTMChQRISnqOL8FwAKA7u7u6OnpqTjdpYuXccHq0af/+lMrz2+sent7qRTrSO+qUE9zp/eX5rOp9rkUpah4nCdDG0ue1Pp9r0WzP5cBZcujwdw1adZ8W1JXCul5a2rfBEzJTXdAaqvWbtbOnCfWEVyImTXfcmDgjK7ZwLJc+2nprLCjgB2pa+ZGYIakieng4xmpzaydOU+sI7hr0qyBJH0V6AH2lbSR7Kyu+cBSSWcCDwMnpclXAMcDfcDTwBkAEbFN0nnAnWm6T0fE4AObzVqW88Q6WcMKMUnrgSeBZ4H+iOhOpxd/DZgKrAdOiojtkgRcTJZcTwOnR8TdjYrNrFki4pQqo46uMG0AZ1WZz0JgYR1DMysN54l1skZ3Tb49Ig6NiO70elTXhTEzMzNrZ80+Rmy014UxMzMza1uNPEYsgJvSKcdfSKcLj/a6MJtzbSO+9ks9r4nSzGusePljW36jruNkZmbWKI0sxN4aEZskvRJYKelH+ZG1XBdmpNd+qee1h5p5jRUvf2zLb9R1nMzMzBqlYV2TEbEpPW8FriO799dorwtjZmZm1rYasqtD0njgJRHxZBqeAXya568LM58XXxfmbElLyG7YuiPXhWlmJTG1xr3N6+efUOdIzMrLeWKj0ag+py7guuyqFOwCfCUiviXpTkZxXRgzMzOzdtaQQiwi1gFvrND+GKO8LoyZtb7h9hDMnd5fyH0lzcqk1j1p1tp8ZX1rG2P5J+YuATMzK4LvNWlmZmZWEBdiZmZmZgVx16SZWR34TDmzoTlHKvMeMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzK4ik9ZJWS7pH0l2pbR9JKyWtTc8TU7skXSKpT9K9kg4vNnqz5nCeWLvzwfpmxXp7RDyaez0PuDki5kual15/DDgOmJYeRwKXp2drcUMdwDzUhW7b/QDmQZwnHazdD/L3HjGzcpkFLErDi4ATc+1XR+Y2YIKkSQXEZ1YGzhNrG94jZlacAG6SFMAXImIB0JW74f0jZPdtBZgMbMi9d2Nq25xrQ9IcYA5AV1cXvb29FRfctUe2t6UsyhRPq8RS7W/bSDt37ixiuc6TBmrndRz4uxb0vR0xF2JmxXlrRGyS9EpgpaQf5UdGRKQfnxFLP1ILALq7u6Onp6fidJcuXsYFq8uT/nOn95cmnlaJZf2pPc0NhuyHrdp3qoGcJw1Upu97vQ3kSEHf2xFz16RZQSJiU3reClwHHAFsGehKSc9b0+SbgCm5tx+Q2szamvPE2l17lsFmJSdpPPCSiHgyDc8APg0sB2YD89PzsvSW5cDZkpaQHXy8I9c1Yx2o3Q9gBueJjc1Ajgx10kslzc4RF2JmxegCrpMEWR5+JSK+JelOYKmkM4GHgZPS9CuA44E+4GngjOaHbNZ0zhNrey7EzAoQEeuAN1Zofww4ukJ7AGc1ITSz0nCeWCfwMWJmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBdml6ADMzKx5ps67oab3rZ9/Qp0jMSunWnMEassT7xEzMzMzK0hpCjFJMyU9KKlP0ryi4zErG+eI2fCcJ9ZqSlGISRoHXAYcBxwMnCLp4GKjMisP54jZ8Jwn1opKUYgBRwB9EbEuIn4FLAFmFRyTWZk4R8yG5zyxlqOIKDoGJL0XmBkRf55efwA4MiLOHjTdHGBOevk64MEmhLcv8GgTluPll3f54yNivwJjaESOFP25DlameBxLddXiObDoHIGOyJNG8Do2T8U8aamzJiNiAbCgmcuUdFdEdDdzmV5+6ZY/tajlj9ZIc6Toz3WwMsXjWKorWzy1atU8aQSvY/HK0jW5CZiSe31AajOzjHPEbHjOE2s5ZSnE7gSmSTpI0m7AycDygmMyKxPniNnwnCfWckrRNRkR/ZLOBm4ExgELI2JNwWENaGpXqJfv5VfSgBwpxXrllCkex1Jd2eJ5gQ7Ik0bwOhasFAfrm5mZmXWisnRNmpmZmXUcF2JmZmZmBenoQkzSFEm3SLpf0hpJH0nt50raJOme9Dg+956Pp1tnPCjp2DrEsF7S6rScu1LbPpJWSlqbniemdkm6JC3/XkmHj3HZr8ut4z2SnpD00Uavv6SFkrZKui/XNup1ljQ7Tb9W0uwxLPv/SfpRmv91kiak9qmSfpH7HD6fe8+b0t+tL8WnWj6LIqhJt4Cp13e7jn/nui17tH//KvGMOs+q/e2UHZx+e2r/mrID1avFUu3/XmGfT9k0K0capcjca5Sy5XRdRUTHPoBJwOFpeC/gx2S3xTgX+LsK0x8M/BDYHTgI+AkwbowxrAf2HdT2z8C8NDwP+GwaPh74JiDgKOD2On4W44BHgAMbvf7A24DDgftqXWdgH2Bdep6YhifWuOwZwC5p+LO5ZU/NTzdoPnekeJTiO67o7/Mo/s4/AV4N7Jb+ngc3aFlj/m7X+e9ct2WP9u9fJZ5R5dlQfztgKXByGv488JdDxFLt/15hn0+ZHs3MkQauQ2G518B1KlVO1/PR0XvEImJzRNydhp8EHgAmD/GWWcCSiHgmIh4C+shuqVFvs4BFaXgRcGKu/erI3AZMkDSpTss8GvhJRDw8TFxjXv+IuBXYVmHeo1nnY4GVEbEtIrYDK4GZtSw7Im6KiP708jayaw9VlZb/8oi4LbIMvjoXb9kVfQuYwv7O9Vp2LX//KvFUUy3PKv7t0pb7O4CvV1i3SrFU+79X2OdTMkXnSKM0JfcapWw5XU8dXYjlSZoKHAbcnprOTrs0Fw7s7iT7Z7Uh97aNDF24jUQAN0lapey2GwBdEbE5DT8CdDVw+QNOBr6ae92s9R8w2nVuVCwfJNsaGnCQpB9I+i9Jf5CLaWMDlt0MjfwbDlaP73Y9463Xsuv59x9NnlVrfwXweG5jYsTxDPq/V8bPpwjNzJFGKVvuNUpbfGddiAGS9gSuBT4aEU8AlwO/DRwKbAYuaODi3xoRhwPHAWdJelt+ZKrOG3qNkXQ8yR8B/56amrn+L9KMda5E0ieBfmBxatoMvCoiDgP+FviKpJc3O64WVvh3u5oil51TaJ5V+L/3nJJ8Pla70uZeo7TyOnV8ISZpV7J/Rosj4hsAEbElIp6NiN8AV/B891vdb58REZvS81bgurSsLQNdjul5a6OWnxwH3B0RW1IsTVv/nNGuc11jkXQ68C7g1JTQpK6hx9LwKrLjRl6blpPvvmyl26g07RYwdfpu1zPeei27Ln//GvKsWvtjZF0vuwxqr6rS/z1K9vkUqOVvk1TC3GuUtvjOdnQhlo6tuBJ4ICIuzLXnj7t6DzBwlsZy4GRJu0s6CJhGdoBfrcsfL2mvgWGyg8bvS8sZOJtjNrAst/zT0hkhRwE7crtlx+IUct2SzVr/QUa7zjcCMyRNTF06M1LbqEmaCfwD8EcR8XSufT9J49Lwq8nWd11a/hOSjkrfodNy8ZZdU24BU8fvdt3+zvVadr3+/jXkWcW/XdpwuAV4b4V1q7Tciv/3KNnnU6CWvk1SSXOvUdrjOxslOMOjqAfwVrJdmfcC96TH8cA1wOrUvhyYlHvPJ8n2jDzIGM+qIDsr54fpsQb4ZGp/BXAzsBb4NrBPahdwWVr+aqC7Dp/BeLIt6r1zbQ1df7KibzPwa7K++DNrWWey47n60uOMMSy7j+y4gYHvwOfTtH+S/i73AHcD787Np5vsn9tPgM+R7lLRCo/0Hf9xiv2TDVpG3b7bdfw7123Zo/37V4ln1HlW7W+XPu87Upz/Duw+RCzV/u8V9vmU7dGMHGlg7IXmXgPXq1Q5Xc+Hb3FkZmZmVpCO7po0MzMzK5ILMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLsQaQNFVSSNolvf6mpNkjfG+vpD9vbITFLlPSJyR9sVnLs3IpS35IOlXSTfWYl1kjOWdeEMPnJf3vImOoNxdiYyRpvaRfSNo58AD2z08TEcdFxKI6LOsFyZjaTpf0bG75D0n6kqTXjnV5NcTXm+J746D261J7D0BE/FNENLXYtGIUmR+p4B9Y7i8H5cmaiFgcETPGutwqsayX9M5BbadL+l4jlmfto8Nz5leS9h3U/oMU41SAiPhwRJzXiBiK4kKsPt4dEXsOPICfNXn530/L3Rt4J/ALYJWkNzQ5DoAfA6cNvJD0CuDNwM8LiMXKoZD8SAX/wDI/TMqT9DikGTE0Q37DzNpGp+bMQ8ApAy8kTQde1oTlFsqFWBPkdw1LGifpAkmPpr1XZw/eywUcKOm/JT0p6abcFsKt6fnxtIXy5vxyIuLZiPhJRPwV8F/AubkYjpL0P5Iel/TDgb1TFWL9bUnfkfRYinGxpAlp3N9LunbQ9JdIujjXtBj4U0nj0utTgOuAX+Xec66kL6fhgS2y2ZJ+mpb5yeE+U2sfzcqPCst9wR6qtJy/krQ2zfu8lA//I+kJSUsl7Zab/l2S7kk59T+SfneU6/36tO6PS1oj6Y8qfSZDxHqWpLXA2tEs11pfG+fMNeQ25IHZwNWDYrhK0mfScI+kjZLmStoqabOkM0bwEZaKC7Hm+wvgOOBQ4HDgxArTvB84A3glsBvwd6n9bel5QtpC+f4Qy/kG8AcAkiYDNwCfAfZJ87tW0n4V3ifg/5LtCn89MIXnC7ovAzNzhdkuwMm8MFF+BtwPDOy+Pm3Q+GreCrwOOBr4P5JeP4L3WPtpVn5UcyzwJuAo4B+ABcCfkeXBG0hb65IOAxYCHwJeAXwBWC5p95EsRNKuwH8CN6X1+GtgsaTXjSLWE4EjgYNH8R5rP+2UM7cBL08bKePIfl++PMzyf4usN2gycCZwmaSJNaxHYVyI1cd/pAr/cUn/Mcy0JwEXR8TGiNgOzK8wzZci4scR8QtgKVmCjdbPyIouyJJiRUSsiIjfRMRK4C7g+MFvioi+iFgZEc9ExM+BC4E/TOM2k21BvS9NPhN4NCJWDZrN1cBpkn6HLMFHktyfiohfRMQPgR8CbxzuDdYyypgf1fxzRDwREWuA+4CbImJdROwAvgkclqabA3whIm5Pe6IXAc+Q/RgNyK/348C/5cYdBewJzI+IX0XEd4DryXXLjMD/jYht6XOw9tKpOQPP7xU7BngA2DTM8n8NfDoifh0RK4CdZBv1LcOFWH2cGBET0uPEYabdH9iQe72hwjSP5IafJvuHPVqTgW1p+EDgfYN+FN4KTBr8JkldkpZI2iTpCbKtkfzBk4vICjvS8zUVlv0N4B3A2VXGV1KPdbZyKmN+VLMlN/yLCq8HlnUgMHdQTk3hhQdV59d7AvBXuXH7Axsi4je5tofJ8nakKn021h46NWcg+814P3A6I+tNeSwi+nOvW+73w4VY820GDsi9njKK98Yopn0P8N00vAG4Jv+jEBHjI6LSltM/peVMj4iXkxVbyo3/D+B3lZ0I8C6yY8JeGGTE02RbQn/JyAsxM2hefozVBuD8QTn1soj46gjf/zNgiqT8/+BX8fzW/1O88CDl36owj2aur5VXW+VMRDxMdtD+8WQb9W3PhVjzLQU+ImlyOtbqY6N478+B3wCvrjQyHbR5kKRLgR7gU2nUl4F3Szo2TfPSdJDjARVmsxfZrt0d6diyv8+PjIhfAl8HvgLcERE/rRLrJ4A/jIj1o1g/s4blR51dAXxY0pHKjJd0gqS9Rvj+28m23P9B0q7KTp55N7Akjb8H+GNJL5P0GrJjX8wqacecORN4R0Q81YS4CudCrPmuIDtA917gB8AKoB94drg3pj1N5wP/nXbtDvStv1nZtWaeAHqBlwO/FxGr0/s2ALPIiqOfk22Z/D2V//6fIjvgcwfZAf6VtkgWAdMZYm9XRPwsInzNJButRuRH3UXEXWQHSX8O2A70kXWljPT9vyIrvI4DHiU7fuy0iPhRmuQisjONt5Dl24v2PJslbZcz6ez/uxoVS9kownu3iyTpOODzEXFg0bGMlKRXAT8Cfisinig6HmtfrZgfZkVyzrQe7xFrMkl7SDpe0i6p6+8csutstYR0TMvfAktchFm9tXp+mDWbc6b1eY9Yk0l6GdnFVn+H7IySG4CPtEJRI2k8WVfJw8DM1OVpVjetnB9mRXDOtD4XYmZmZmYFcdekmZmZWUFciJmZmZkVZJfhJymnfffdN6ZOnVpx3FNPPcX48eObG9AIObbaFBnbqlWrHo2ISvflLLVWzZFqWi3mToq3VXMEWi9PHNPIlDGmqnkSES35eNOb3hTV3HLLLVXHFc2x1abI2IC7ogTf+dE+WjVHqmm1mDsp3lbNkWjBPHFMI1PGmKrlibsmzczMzAriQszMzMysIC7EzMzMzAoybCEmaaGkrZLuy7XtI2mlpLXpeWJql6RLJPVJulfS4bn3zE7Tr5U0O9f+Jkmr03sukaR6r6SZmZlZGY1kj9hVwMxBbfOAmyNiGnBzeg3ZDWynpccc4HLICjey2y4cCRwBnDNQvKVp/iL3vsHLMjMzM2tLw16+IiJulTR1UPMsoCcNLwJ6gY+l9qvT2QG3SZogaVKadmVEbAOQtBKYKakXeHlE3JbarwZOBL45lpVavWkHp8+7YdTvWz//hLEs1syqmFpDPoJz0jrLSPJk7vT+F/2+OU9aW63XEeuKiM1p+BGgKw1PBvL3H9yY2oZq31ihvSJJc8j2tNHV1UVvb2/l4PbIvqyjVW1+9bRz586mLKcWjs3MzKy5xnxB14gISU25YWVELAAWAHR3d0dPT0/F6S5dvIwLVo9+1dafWnl+9dTb20u1uIvm2MzMzJqr1rMmt6QuR9Lz1tS+CZiSm+6A1DZU+wEV2s3MzMzaXq2F2HJg4MzH2cCyXPtp6ezJo4AdqQvzRmCGpInpIP0ZwI1p3BOSjkpnS56Wm5eZmZlZWxu2/07SV8kOtt9X0kaysx/nA0slnQk8DJyUJl8BHA/0AU8DZwBExDZJ5wF3puk+PXDgPvBXZGdm7kF2kP6YDtQ3MzMzaxUjOWvylCqjjq4wbQBnVZnPQmBhhfa7gDcMF4eZmZlZu/GV9c0aSNJLJd0h6YeS1kj6VGo/SNLt6ULGX5O0W2rfPb3uS+On5ub18dT+oKRjC1ols7pyjlincyFm1ljPAO+IiDcCh5JdP+8o4LPARRHxGmA7cGaa/kxge2q/KE2HpIOBk4FDyC56/G+SxjVzRcwaxDliHc2FmFkDRWZnerlregTwDuDrqX0R2YWMIbso8qI0/HXg6HQiyyxgSUQ8ExEPkR2HeUTj18CssZwj1unGfB0xMxta2ipfBbwGuAz4CfB4RAxcdTh/IePnLn4cEf2SdgCvSO235WZb8eLHI73ocbMvkFvLBZbhhRdZbrWL+jrekWtmjqTltWyeVLpgedHfszJ+18sYUzUuxMwaLCKeBQ6VNAG4DvidBi5rRBc9bvYFcmu55Ri88CLLrXZRX8c7cs3MkbS8ls2TudP7X3TB8mZcjHwoZfyulzGmatw1adYkEfE4cAvwZmCCpIH/pvkLGT938eM0fm/gMapfFNmsbThHrBO5EDNrIEn7pa18JO0BHAM8QPZj89402eCLIg9cLPm9wHfSZWGWAyenM8YOAqYBdzRlJcwayDlinc5dk2aNNQlYlI6BeQmwNCKul3Q/sETSZ4AfAFem6a8ErpHUB2wjOwuMiFgjaSlwP9APnJW6c8xanXPEOpoLMbMGioh7gcMqtK+jwhldEfFL4H1V5nU+cH69YzQrknPEOp0LMbMOtHrTjpoOoF8//4QGRGNWTs4TawYfI2ZmZmZWEBdiZmZmZgVxIWZmZmZWEBdiZmZmZgVxIWZmZmZWEBdiZmZmZgVxIWZmZmZWEBdiZmZmZgVxIWZmZmZWkDEVYpL+l6Q1ku6T9FVJL5V0kKTbJfVJ+pqk3dK0u6fXfWn81Nx8Pp7aH5R07BjXyczMzKwl1FyISZoM/A3QHRFvAMaR3Xz1s8BFEfEaYDtwZnrLmcD21H5Rmg5JB6f3HQLMBP4t3fzVzMzMrK2NtWtyF2APSbsALwM2A+8Avp7GLwJOTMOz0mvS+KMlKbUviYhnIuIhoI8KN3o1MzMzazc13/Q7IjZJ+hfgp8AvgJuAVcDjEdGfJtsITE7Dk4EN6b39knYAr0jtt+VmnX/PC0iaA8wB6Orqore3t2JsXXvA3On9FccNpdr86mnnzp1NWU4tHFv9SZoCXA10AQEsiIiLJZ0L/AXw8zTpJyJiRXrPx8n2ID8L/E1E3JjaZwIXk+19/mJEzG/muhRhau6Gy3On94/4Bsy+6XLrcI6M3dQabkwOzpOyqLkQkzSRbG/WQcDjwL+TdS02TEQsABYAdHd3R09PT8XpLl28jAtWj37V1p9aeX711NvbS7W4i+bYGqIfmBsRd0vaC1glaWUad1FE/Et+4kFd9fsD35b02jT6MuAYso2VOyUtj4j7m7IWZo3jHLGOVnMhBrwTeCgifg4g6RvAW4AJknZJe8UOADal6TcBU4CNqStzb+CxXPuA/HvMWlpEbCbrsicinpT0AFX2+CbPddUDD0nKd9X3RcQ6AElL0rT+kbGW5hyxTjeWQuynwFGSXkbWNXk0cBdwC/BeYAkwG1iWpl+eXn8/jf9ORISk5cBXJF1ItnUzDbhjDHGZlVI6U/gw4HayjZazJZ1GljdzI2I7Q3fVbxjUfmSFZZSy+76WZQ02mpjL0I3dat3pZYi3GTmSltOyeVJrTJXU6+9dhu/OYGWMqZqxHCN2u6SvA3eT7Vr+AVm34Q3AEkmfSW1XprdcCVyTtl62ke1aJiLWSFpKttXSD5wVEc/WGpdZGUnaE7gW+GhEPCHpcuA8smNizgMuAD441uWUtft+pMd2DWXu9P4Rx9yMwwyG02rd6UXH26wcgdbOk9HkwXDqlSdFf3cqKWNM1YzprxkR5wDnDGpeR4WzHiPil8D7qsznfOD8scRiVlaSdiX7gVkcEd8AiIgtufFXANenl0N11bsL39qSc8Q6ma+sb9ZA6RItVwIPRMSFufZJucneA9yXhpcDJ6cLIB/E8131dwLT0gWTdyPbo7y8Getg1kjOEet09dm/aWbVvAX4ALBa0j2p7RPAKZIOJet2WQ98CIbuqpd0NnAj2an5CyNiTfNWw6xhnCPW0VyImTVQRHwPUIVRK4Z4T8Wu+nQNparvM2tFzhHrdO6aNDMzMyuICzEzMzOzgrgQMzMzMyuICzEzMzOzgrgQMzMzMyuICzEzMzOzgrgQMzMzMyuICzEzMzOzgrgQMzMzMyuICzEzMzOzgrgQMzMzMyuICzEzMzOzgrgQMzMzMyuICzGzBpI0RdItku6XtEbSR1L7PpJWSlqbniemdkm6RFKfpHslHZ6b1+w0/VpJs4taJ7N6co5Yp3MhZtZY/cDciDgYOAo4S9LBwDzg5oiYBtycXgMcB0xLjznA5ZD9KAHnAEcCRwDnDPwwmbU454h1NBdiZg0UEZsj4u40/CTwADAZmAUsSpMtAk5Mw7OAqyNzGzBB0iTgWGBlRGyLiO3ASmBm89bErDGcI9bpdik6ALNOIWkqcBhwO9AVEZvTqEeArjQ8GdiQe9vG1FatffAy5pDtJaCrq4ve3t6KsXTtAXOn9496HarNbzi1LGuw0cRca5z1tHPnzlLEMVJliLcZOZKW07J5UmtMldTr712G785gZYypmjEVYpImAF8E3gAE8EHgQeBrwFRgPXBSRGyXJOBi4HjgaeD0ga2g1Jf/j2m2n4mIRZi1EUl7AtcCH42IJ7J0yERESIp6LCciFgALALq7u6Onp6fidJcuXsYFq0ef/utPrTy/4Zw+74aa3pc3d3r/iGOuNc566u3tpdrnX0ZFx9usHEnza9k8GU0eDKdeeVL0d6eSMsZUzVi7Ji8GvhURvwO8kWyXsvv1zXIk7Ur2A7M4Ir6Rmrek7hTS89bUvgmYknv7AamtWrtZy3OOWCeruRCTtDfwNuBKgIj4VUQ8jvv1zZ6T9gRfCTwQERfmRi0HBs7qmg0sy7Wfls4MOwrYkbpnbgRmSJqYNlRmpDazluYcsU43lv2bBwE/B74k6Y3AKuAjdGC//miUud/asTXEW4APAKsl3ZPaPgHMB5ZKOhN4GDgpjVtB1n3fR9aFfwZARGyTdB5wZ5ru0xGxrSlrYNZYzhHraGMpxHYBDgf+OiJul3Qxz3dDAp3Trz8aZe63dmz1FxHfA1Rl9NEVpg/grCrzWggsrF90ZsVzjlinG8sxYhuBjRFxe3r9dbLCzP36ZmZmZiNQcyEWEY8AGyS9LjUdDdyP+/XNzMzMRmSs58D+NbBY0m7AOrK++pfgfn0zMzOzYY2pEIuIe4DuCqPcr29mZmY2DN/iyMzMzKwgLsTMzMzMCuJCzMzMzKwgLsTMzMzMCuJCzMzMzKwgLsTMzMzMCuJCzMzMzKwgY72gq5l1kKnzbig6BLPSc57YaHiPmJmZmVlBXIiZmZmZFcSFmFkDSVooaauk+3Jt50raJOme9Dg+N+7jkvokPSjp2Fz7zNTWJ2les9fDrFGcI9bpfIyYWWNdBXwOuHpQ+0UR8S/5BkkHAycDhwD7A9+W9No0+jLgGGAjcKek5RFxfyMDb2VjOUZn/fwT6hiJjcBVOEcKUWueOEfqy4WYWQNFxK2Spo5w8lnAkoh4BnhIUh9wRBrXFxHrACQtSdP6R8ZannPEOp0LMbNinC3pNOAuYG5EbAcmA7flptmY2gA2DGo/stJMJc0B5gB0dXXR29tbceFde8Dc6f1jib/pmhVztc9stHbu3Fm3eTVDCeNtSI5Aa+dJGWIa/HmV8LtTypiqcSFm1nyXA+cBkZ4vAD5YjxlHxAJgAUB3d3f09PRUnO7Sxcu4YHVrpf/c6f1NiXn9qT11mU9vby/VPv8yKlm8DcsRaO08aVYeDGVwjpTsuwOUM6ZqyvUNM+sAEbFlYFjSFcD16eUmYEpu0gNSG0O0m7Ud54h1Ep81adZkkiblXr4HGDhbbDlwsqTdJR0ETAPuAO4Epkk6SNJuZAcrL29mzGbN5ByxTuI9YmYNJOmrQA+wr6SNwDlAj6RDybpd1gMfAoiINZKWkh1g3A+cFRHPpvmcDdwIjAMWRsSa5q6JWWM4R6zTuRAza6CIOKVC85VDTH8+cH6F9hXAijqGZlYKzhHrdGPumpQ0TtIPJF2fXh8k6fZ0Ub2vpd3EpF3JX0vtt+dPV652gT4zMzOzdlaPY8Q+AjyQe/1ZsgvxvQbYDpyZ2s8Etqf2i9J0gy/QNxP4N0nj6hCXmZmZWamNqRCTdABwAvDF9FrAO4Cvp0kWASem4VnpNWn80Wn65y7QFxEPAfkL9JmZmZm1rbEeI/avwD8Ae6XXrwAej4iBq83lL7Y3mXTBvYjol7QjTT/UBfpeoNEX4WvGxd/KfJE5x2ZmZtZcNRdikt4FbI2IVZJ66hbREBp9Eb56XchxKGW+yJxjMzMza66x7BF7C/BHko4HXgq8HLgYmCBpl7RXLH9RvYEL8W2UtAuwN/AYQ1+gz8zMzKxt1XyMWER8PCIOiIipZAfbfyciTgVuAd6bJpsNLEvDy9Nr0vjvRERQ/QJ9ZmZmZm2tEdcR+xiwRNJngB/w/PVgrgSukdQHbCMr3oa8QJ+ZmZlZO6tLIRYRvUBvGl5HhbMeI+KXwPuqvL/iBfrMzMzM2pnvNWlmZmZWEBdiZmZmZgVxIWZmZmZWEBdiZmZmZgVxIWbWQJIWStoq6b5c2z6SVkpam54npnZJukRSn6R7JR2ee8/sNP1aSbMrLcusVTlPrJO5EDNrrKvIbmafNw+4OSKmATen1wDHkV1HbxrZrbwuh+wHCTgHOJLsjORzBn6UzNrEVThPrEO5EDNroIi4ley6eXmzgEVpeBFwYq796sjcRnaXiknAscDKiNgWEduBlbz4R8usZTlPrJM14oKuZja0rojYnIYfAbrS8GRgQ266jamtWvuLSJpDtpeArq6uqjdK79oD5k7vrzH8YjQr5nrdXL7VblRfwnidJxWUIabBn1cJvzuljKkaF2JmBYqIkBR1nN8CYAFAd3d3VLtR+qWLl3HB6tZK/7nT+5sS8/pTe+oyn1a7UX2Z43WePK9ZeTCUwTlSxu9OGWOqxl2TZs23JXWlkJ63pvZNwJTcdAektmrtZu3MeWIdwYWYWfMtBwbO6JoNLMu1n5bOCjsK2JG6Zm4EZkiamA4+npHazNqZ88Q6Qrn2uZq1GUlfBXqAfSVtJDuraz6wVNKZwMPASWnyFcDxQB/wNHAGQERsk3QecGea7tMRMfjAZrOW5TyxTuZCzKyBIuKUKqOOrjBtAGdVmc9CYGEdQzMrDeeJdTJ3TZqZmZkVxIWYmZmZWUFciJmZmZkVxIWYmZmZWUFciJmZmZkVxIWYmZmZWUF8+Qozs5yp826o6X3r559Q50jMymlwjsyd3s/pI8gb50hlNe8RkzRF0i2S7pe0RtJHUvs+klZKWpueJ6Z2SbpEUp+keyUdnpvX7DT9Wkmzqy3TzMzMrJ2MpWuyH5gbEQcDRwFnSToYmAfcHBHTgJvTa4DjgGnpMQe4HLLCjewqykcCRwDnDBRvZmZmZu2s5kIsIjZHxN1p+EngAWAyMAtYlCZbBJyYhmcBV0fmNmBCupHrscDKiNgWEduBlcDMWuMyMzMzaxV1OUZM0lTgMOB2oCvdgBXgEaArDU8GNuTetjG1VWuvtJw5ZHvT6Orqore3t2I8XXtkfdajVW1+9bRz586mLKcWjs3MzKy5xlyISdoTuBb4aEQ8Iem5cRERkmKsy8jNbwGwAKC7uzt6enoqTnfp4mVcsHr0q7b+1Mrzq6fe3l6qxV00x2ZmZtZcY7p8haRdyYqwxRHxjdS8JXU5kp63pvZNwJTc2w9IbdXazczMzNraWM6aFHAl8EBEXJgbtRwYOPNxNrAs135aOnvyKGBH6sK8EZghaWI6SH9GajNra5LWS1ot6R5Jd6W2UZ91bNbOnCfW7sayR+wtwAeAd6QEuUfS8cB84BhJa4F3ptcAK4B1QB9wBfBXABGxDTgPuDM9Pp3azDrB2yPi0IjoTq9HddaxWYdwnljbqvkYsYj4HqAqo4+uMH0AZ1WZ10JgYa2xmLWRWUBPGl4E9AIfI3fWMXCbpAmSJuVOjDHrJM4Taxu+sr5ZcQK4KZ3Q8oV0Mspozzp+wQ9Mo88sLlLZYx78Wbfamb4ljtd5ktPKMTXz+1Xi7/OLuBAzK85bI2KTpFcCKyX9KD+ylrOOG31mcZHmTu8vdcyDz7putTN9Sxyv8ySnjHkw0piacWWCASX+Pr+Ib/ptVpCI2JSetwLXkd1ZYrRnHZu1NeeJtTsXYmYFkDRe0l4Dw2RnC9/H6M86NmtbzhPrBOXav2nWObqA69IFkHcBvhIR35J0J7BU0pnAw8BJafoVwPFkZx0/DZzR/JDNms55Ym3PhZhZASJiHfDGCu2PMcqzjs3alfPEOoG7Js3MzMwK4kLMzMzMrCAuxMzMzMwK4mPEzMzqYOq8G17weu70fk4f1FbJ+vknNCoks1IZnCMj1e454j1iZmZmZgVxIWZmZmZWEBdiZmZmZgVxIWZmZmZWEBdiZmZmZgXxWZNmZgXymWRmQ6slR+ZO76en/qE0hAsxaxj/wJiZmQ3NhVgdjKbgyF9bqNkFx3BxVrvuUasURrUWftA662hmZu3FhZgNaywFjpmZmVVXmkJM0kzgYmAc8MWImN/sGJpdcLjAqazS5zLSq5S3szLkiFnZOU+s1ZSiEJM0DrgMOAbYCNwpaXlE3F9sZGbl4ByxwXwM5os5TyyvVXKkLJevOALoi4h1EfErYAkwq+CYzMrEOWI2POeJtZxS7BEDJgMbcq83AkcWFItZGTlHrC6q7SUYrvu/RfakOU9szJp94ldZCrERkTQHmJNe7pT0YJVJ9wUebU5Uo/M3jq0mjY5Nnx1y9IGNWm69tUOOVFPm72cl7RZvu+QItHaelPF75ZieV0uelKUQ2wRMyb0+ILW9QEQsABYMNzNJd0VEd/3Cqx/HVpsyx9YkHZMj1bRazI63EG2fJ45pZMoYUzVlOUbsTmCapIMk7QacDCwvOCazMnGOmA3PeWItpxR7xCKiX9LZwI1kpxwvjIg1BYdlVhrOEbPhOU+sFZWiEAOIiBXAijrNbthdzgVybLUpc2xN0UE5Uk2rxex4C9ABeeKYRqaMMVWkiCg6BjMzM7OOVJZjxMzMzMw6TlsVYpJmSnpQUp+keQUsf6GkrZLuy7XtI2mlpLXpeWJql6RLUqz3Sjq8wbFNkXSLpPslrZH0kbLEJ+mlku6Q9MMU26dS+0GSbk8xfC0dfIuk3dPrvjR+aqNia0dF58lwRpNHZTDa3CqD0eZcpykyRyStl7Ra0j2S7kptTf0/Xa/fMkmz0/RrJc1uQEznStqUPqt7JB2fG/fxFNODko7NtZfv/19EtMWD7MDMnwCvBnYDfggc3OQY3gYcDtyXa/tnYF4angd8Ng0fD3wTEHAUcHuDY5sEHJ6G9wJ+DBxchvjSMvZMw7sCt6dlLgVOTu2fB/4yDf8V8Pk0fDLwtaK/f63yKEOejCDGEedRGR6jza0yPEabc530KDpHgPXAvoPamvp/uh6/ZcA+wLr0PDENT6xzTOcCf1dh2oPT32134KD09xxX9N+22qOd9ogVfmuLiLgV2DaoeRawKA0vAk7MtV8dmduACZImNTC2zRFxdxp+EniA7CrUhceXlrEzvdw1PQJ4B/D1KrENxPx14GhJakRsbajwPBnOKPOocDXkVuFqyLlOUsYcaer/6Tr9lh0LrIyIbRGxHVgJzKxzTNXMApZExDMR8RDQR/Z3LePftq0KsUq3tphcUCx5XRGxOQ0/AnSl4cLiTV15h5FtBZciPknjJN0DbCVL2J8Aj0dEf4XlPxdbGr8DeEWjYmszZc2T4VT7npbKCHOrFEaZc52k6BwJ4CZJq5TdAQDK8X96tDE0K7azU5fowlz3f9ExjUo7FWKlF9k+00JPU5W0J3At8NGIeCI/rsj4IuLZiDiU7ErYRwC/U0QcVn5lyKNKyppb1TjnSuutEXE4cBxwlqS35UeW4btUhhiSy4HfBg4FNgMXFBpNjdqpEBvRrS0KsGVgV3F63pramx6vpF3JfigWR8Q3yhYfQEQ8DtwCvJlsF/fAte7yy38utjR+b+CxRsfWJsqaJ8Op9j0thVHmVqmMMOc6SaE5EhGb0vNW4DqyIrkM/6dHG0PDY4uILWmD4jfAFWSfVaEx1aKdCrGy3tpiOTBwtshsYFmu/bR0xslRwI7cbt+6S8dQXQk8EBEXlik+SftJmpCG9wCOITvO5hbgvVViG4j5vcB30haaDa+seTKcat/TwtWQW4WrIec6SWE5Imm8pL0GhoEZwH2U4P90DTHcCMyQNDF1Gc5IbXUz6Hi495B9VgMxnazsDPuDgGnAHZT1/1+RZwrU+0F29saPyY51+GQBy/8q2e7RX5P1PZ9JduzSzcBa4NvAPmlaAZelWFcD3Q2O7a1ku5LvBe5Jj+PLEB/wu8APUmz3Af8ntb+aLHn6gH8Hdk/tL02v+9L4Vxf93WulR9F5MoL4RpxHZXiMNrfK8BhtznXao6gcSZ//D9NjzcCym/1/ul6/ZcAH03epDzijATFdk5Z5L1lBNSk3/SdTTA8CxxX9tx3q4Svrm5mZmRWknbomzczMzFqKCzEzMzOzgrgQMzMzMyuICzEzMzOzgrgQMzMzMyuICzEzMzOzgrgQMzMzMyuICzEzMzOzgrgQMzMzMyuICzEzMzOzgrgQMzMzMyuICzEzMzOzgrgQMzMzMyuICzEzMzOzgrgQK4ikqyR9pknL+ktJWyTtlPSKJi1zqqSQtEszlmdmZtaKXIi1OEm/L+k7kp6UtEPSf0o6ODd+V+BCYEZE7An8k6TL8+MlPVWl7aimroyZmVmHcSHWwiS9GbgJWAbsDxwE/BD4b0mvTpN1AS8F1qTXtwJvy82mG/gp8AeD2gBWNSZyMzMzAxdiTSPpMEl3pz1XXyMrjpA0UdL1kn4uaXsaPiCNe5+kVYPm87eSlqWX/wxcHREXR8STEbEtIv4RuA04V9JrgQfTtI9L+g5ZIfZ6Sfum9j8AlgDjB7V9PyJ+LWl/Sdem+B6S9De5WF4iaZ6kn0h6TNJSSftUWf8/kbRe0hvG9kmamZm1DxdiTSBpN+A/gGuAfYB/B/4kjX4J8CXgQOBVwC+Az6Vxy4GDJL0+N7sPAFdLehnw+2legy0FjomIHwOHpLYJEfGOiNgAPMzze8DeBnwX+J9BbbdKegnwn2R72SYDRwMflXRsmu6vgROBPyTbI7cduKzC+p8BfBZ4Z0TcV+1zMjMz6zQuxJrjKGBX4F8j4tcR8XXgToCIeCwiro2IpyPiSeB8ssKGiHgG+BrwZwCSDgGmAteTFXQvATZXWN5mYN8K7QP+C3hbKrSOINuD9t1c21vSNL8H7BcRn46IX0XEOuAK4OQ0nw8Dn4yIjSnWc4H3DjpA/6PA3wM9EdE3kg/LzMysU7gQa479gU0REbm2hwEkvUzSFyQ9LOkJsq7DCZLGpekWAe+XJLK9YUtT0bMd+A0wqcLyJgGPDhHPwHFi04F1EfE08L1c2x7A7WR76faX9PjAA/gE2XFnpPHX5cY9ADybGw9ZEXZZRGwc8hMyMzPrQC7EmmMzMDkVUwNelZ7nAq8DjoyIl/P8gfQCiIjbgF+RdRu+n6x7k4h4Cvg+8L4KyzsJuHmIeG4F3gicQLYnDLKD+aektjsj4pfABuChiJiQe+wVEcen92wAjhs0/qURsSm3rBnAP0r6E8zMzOwFXIg1x/eBfuBv0qUh/pisSxBgL7Ljwh5PB7qfU+H9V5MdN/briPhern0eMFvS30jaKx34/xngzcCnqgWTugi3AB8hFWJpb93tqe3WNOkdwJOSPiZpD0njJL1B0u+l8Z8Hzpd0IICk/STNGrS4NcBM4DJJfzTkp2RmZtZhXIg1QUT8Cvhj4HRgG/CnwDfS6H8l6wp8lOxYrW9VmMU1wBuALw+a7/eAY9O8N5N1dx4GvDUi1g4T1q3AfsB/59q+C7wyjSMingXeBRwKPJRi/CKwd5r+YrITCm6S9GSK/8gK6//DNJ8rJB03TFxmZmYdQy88bMnKSNIewFbg8BEUWGZmZtYivEesNfwl2XFbLsLMzMzaiO8DWHKS1pMduH9isZGYmZlZvblr0szMzKwg7po0MzMzK0jLdk3uu+++MXXq1IrjnnrqKcaPH9/cgOrEsRdjqNhXrVr1aETs1+SQzMysA7RsITZ16lTuuuuuiuN6e3vp6elpbkB14tiLMVTskh5ubjRmZtYp3DVpZmZmVhAXYmZmZmYFcSFmZmZmVhAXYmZmZmYFqbkQkzRF0i2S7pe0RtJHUvs+klZKWpueJ6Z2SbpEUp+keyUdnpvX7DT9Wkmzx75aZmZmZuU3lrMm+4G5EXG3pL2AVZJWkt3Y+uaImC9pHjAP+BhwHDAtPY4ELgeOlLQPcA7QDUSaz/KI2F5rYKs37eD0eTeM+n3r559Q6yLNzMzMRq3mPWIRsTki7k7DTwIPAJOBWcCiNNkinr81zyzg6sjcBkyQNAk4FlgZEdtS8bUSmFlrXGZmZmatoi7XEZM0FTgMuB3oiojNadQjQFcangxsyL1tY2qr1l5pOXOAOQBdXV309vZWjKdrD5g7vX/U61Ftfs20c+fOUsSRt3rTjhFN17UHXLp42XOvp0/eu1Eh1V0ZP3czM2t/Yy7EJO0JXAt8NCKekPTcuIgISXW7mWVELAAWAHR3d0e1C3BeungZF6we/aqtP7Xy/JqpjBdFHWk379zp/S/43MvweY5UGT93MzNrf2M6a1LSrmRF2OKI+EZq3pK6HEnPW1P7JmBK7u0HpLZq7WZmZmZtbSxnTQq4EnggIi7MjVoODJz5OBtYlms/LZ09eRSwI3Vh3gjMkDQxnWE5I7WZmZmZtbWxdE2+BfgAsFrSPantE8B8YKmkM4GHgZPSuBXA8UAf8DRwBkBEbJN0HnBnmu7TEbFtDHG1jKkVuvzmTu8ftivQZ3eamZm1h5oLsYj4HqAqo4+uMH0AZ1WZ10JgYa2x1EulwmgkXBiZmZlZLepy1qS1hloLzWZzQWxmZp3CtzgyMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OC1FyISVooaauk+3Jt50raJOme9Dg+N+7jkvokPSjp2Fz7zNTWJ2le7atiZmZm1lrGskfsKmBmhfaLIuLQ9FgBIOlg4GTgkPSef5M0TtI44DLgOOBg4JQ0rZmZmVnb26XWN0bErZKmjnDyWcCSiHgGeEhSH3BEGtcXEesAJC1J095fa1xmZmZmraLmQmwIZ0s6DbgLmBsR24HJwG25aTamNoANg9qPrDZjSXOAOQBdXV309vZWnK5rD5g7vb/W+EetWhzDqRTjSGKv5/LqaXDszY6z1uUB7Ny5c0zvNzMzq0W9C7HLgfOASM8XAB+s18wjYgGwAKC7uzt6enoqTnfp4mVcsLoRNWZl60+tHMdwTp93w4va5k7vHzb2ei6vngbH3uw4a10eZEVcte+TmZlZo9S1WomILQPDkq4Ark8vNwFTcpMekNoYot3MzMysrdX18hWSJuVevgcYOKNyOXCypN0lHQRMA+4A7gSmSTpI0m5kB/Qvr2dMZmZmZmVV8x4xSV8FeoB9JW0EzgF6JB1K1jW5HvgQQESskbSU7CD8fuCsiHg2zeds4EZgHLAwItbUGpOZmZlZKxnLWZOnVGi+cojpzwfOr9C+AlhRaxxmZmZmrcpX1jczMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4K4EDMzMzMrSPNuyGjWYFPHcC/Nq2aOr2MkZmZmI+M9YmZmZmYFcSFmZmZmVhAXYmZmZmYFcSFmZmZmVhAXYmZmZmYFcSFmZmZmVhAXYmZmZmYFcSFmZmZmVhAXYmZmZmYFcSFmZmZmVhAXYmZmZmYFcSFmZmZmVhAXYmZmZmYFcSFmZmZmVhAXYmZmZmYFcSFmZmZmVhAXYmZmZmYFcSFmZmZmVpAxFWKSFkraKum+XNs+klZKWpueJ6Z2SbpEUp+keyUdnnvP7DT9WkmzxxKTmZmZWasY6x6xq4CZg9rmATdHxDTg5vQa4DhgWnrMAS6HrHADzgGOBI4Azhko3szMzMza2ZgKsYi4Fdg2qHkWsCgNLwJOzLVfHZnbgAmSJgHHAisjYltEbAdW8uLizszMzKzt7NKAeXZFxOY0/AjQlYYnAxty021MbdXaX0TSHLK9aXR1ddHb21s5gD1g7vT+GsMfvWpxDKdSjCOJvZ7Lq6fBsZc1zkp27txZc7xmZma1akQh9pyICElRx/ktABYAdHd3R09PT8XpLl28jAtWN3TVXmD9qZXjGM7p8254Udvc6f3Dxl7P5dXT4NjLGmclV80cT7Xvk5mZWaM04qzJLanLkfS8NbVvAqbkpjsgtVVrNzMzM2trjSjElgMDZz7OBpbl2k9LZ08eBexIXZg3AjMkTUwH6c9IbWZmZmZtbUz9d5K+CvQA+0raSHb243xgqaQzgYeBk9LkK4DjgT7gaeAMgIjYJuk84M403acjYvAJAGZmZmZtZ0yFWEScUmXU0RWmDeCsKvNZCCwcSyxmZmZmrcZX1jczMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4I0rBCTtF7Sakn3SLorte0jaaWktel5YmqXpEsk9Um6V9LhjYrLzMzMrCwavUfs7RFxaER0p9fzgJsjYhpwc3oNcBwwLT3mAJc3OC4zMzOzwjW7a3IWsCgNLwJOzLVfHZnbgAmSJjU5NjMzM7OmUkQ0ZsbSQ8B2IIAvRMQCSY9HxIQ0XsD2iJgg6XpgfkR8L427GfhYRNw1aJ5zyPaY0dXV9aYlS5ZUXPbWbTvY8ouGrFZF0yfvXdP7Vm/a8aK2rj0YNvZ6Lq+eBsde1jgrOWjvcey5554Vx7397W9fldura2ZmVje7NHDeb42ITZJeCayU9KP8yIgISaOqAiNiAbAAoLu7O3p6eipOd+niZVywupGr9kLrT60cx3BOn3fDi9rmTu8fNvZ6Lq+eBsde1jgruWrmeKp9n8zMzBqlYV2TEbEpPW8FrgOOALYMdDmm561p8k3AlNzbD0htZmZmZm2rIYWYpPGS9hoYBmYA9wHLgdlpstnAsjS8HDgtnT15FLAjIjY3IjYzMzOzsmhU/10XcF12GBi7AF+JiG9JuhNYKulM4GHgpDT9CuB4oA94GjijQXGZmZmZlUZDCrGIWAe8sUL7Y8DRFdoDOKsRsZiZmZmVla+sb2ZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlaQ0hRikmZKelBSn6R5RcdjZmZm1milKMQkjQMuA44DDgZOkXRwsVGZmZmZNVYpCjHgCKAvItZFxK+AJcCsgmMyMzMzayhFRNExIOm9wMyI+PP0+gPAkRFx9qDp5gBz0svXAQ9WmeW+wKMNCrfRHHsxhor9wIjYr5nBmJlZZ9il6ABGIyIWAAuGm07SXRHR3YSQ6s6xF6OVYzczs9ZVlq7JTcCU3OsDUpuZmZlZ2ypLIXYnME3SQZJ2A04Glhcck5mZmVlDlaJrMiL6JZ0N3AiMAxZGxJoxzHLY7ssSc+zFaOXYzcysRZXiYH0zMzOzTlSWrkkzMzOzjuNCzMzMzKwgbVWIteptkiRNkXSLpPslrZH0kaJjGi1J4yT9QNL1RccyGpImSPq6pB9JekDSm4uOyczMOkfbHCOWbpP0Y+AYYCPZmZinRMT9hQY2ApImAZMi4m5JewGrgBNbIfYBkv4W6AZeHhHvKjqekZK0CPhuRHwxnbH7soh4vOCwzMysQ7TTHrGWvU1SRGyOiLvT8JPAA8DkYqMaOUkHACcAXyw6ltGQtDfwNuBKgIj4lYswMzNrpnYqxCYDG3KvN9JCxcwASVOBw4DbCw5lNP4V+AfgNwXHMVoHAT8HvpS6Vb8oaXzRQZmZWedop0Ks5UnaE7gW+GhEPFF0PCMh6V3A1ohYVXQsNdgFOBy4PCIOA54CWubYQjMza33tVIi19G2SJO1KVoQtjohvFB3PKLwF+CNJ68m6g98h6cvFhjRiG4GNETGw9/HrZIWZmZlZU7RTIdayt0mSJLLjlB6IiAuLjmc0IuLjEXFAREwl+8y/ExF/VnBYIxIRjwAbJL0uNR0NtMwJEmZm1vpKcYujemjAbZKa6S3AB4DVku5JbZ+IiBXFhdQx/hpYnIr3dcAZBcdjZmYdpG0uX2FmZmbWatqpa9LMzMyspbgQMzMzMyuICzEzMzOzgrgQMzMzMyuICzEzMzOzgrgQMzMzMyuICzEzMzOzgvx/uOvlNtdfgCUAAAAASUVORK5CYII=\n" 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zxuoH5kbE3ZL2AlZJWpnGXRQR/5KfWNLBwMnAIcD+wLclvTaNvgw4BtgI3ClpeUTc35S1MGsc54h1tJoLMUkLgXcBWyPiDantXOAvgJ+nyT4RESvSuI8DZwLPAn8TETem9pnAxcA44IsRMb/WmFrNSE45nju9n9N9anLLiojNwOY0/KSkB4DJQ7xlFrAkIp4BHpLUBxyRxvVFxDoASUvStP6RsZbmHLFON5Y9YlcBnwOuHtTuLRizCiRNBQ4DbgfeApwt6TTgLrI9AtvJfoBuy71tI8//KG0Y1H5khWXMAeYAdHV10dvbWzGWrj2yIn+0qs1vrHbu3Flx3rXE2KhYilCmWKDx8TQjR9JyWjJPqhlLntSyjmX6To5E2fJosJoLsYi4NSXNSHgLxjqapD2Ba4GPRsQTki4HzgMiPV8AfHCsy4mIBcACgO7u7ujp6ak43aWLl3HB6tGn//pTK89vrHp7e6kUaxF7g6+aOb5iLEWo9rkUpZHxNCtHoHXzpJqx5Mnc6f2jXsdmr99YlS2PBmvEMWIN2YKBkW/F1FMjK+mRbIXUukVWL0UsP/95F70ls3PnzjHPQ9KuZD8wiyPiGwARsSU3/grg+vRyEzAl9/YDUhtDtJu1NOeIdbJ6F2IN24KBkW/F1FMjK+mRbMXUsrVST0UsP7+1VfSWzFiLQEkCrgQeiIgLc+2T0rExAO8B7kvDy4GvSLqQrBt/GnAHIGCapIPIflxOBt4/puDMSsA5Yp2urr+w3oIxe5G3AB8AVku6J7V9AjhF0qFkGy3rgQ8BRMQaSUvJuuf7gbMi4lkASWcDN5Kd2LIwItY0bzXMGsY5Yh2troWYt2DMXigivkf2PR9sxRDvOR84v0L7iqHeZ9aKnCPW6cZy+YqvAj3AvpI2AucAPd6CMTMzMxuZsZw1eUqF5iuHmN5bMGZmZmY5vrJ+HYzkwqxmZmZmg/lek2ZmZmYFcSFmZmZmVhAXYmZmZmYFcSFmZmZmVhAXYmZmZmYFcSFmZmZmVhAXYmZmZmYFcSFmZmZmVhAXYmZmZmYFcSFmZmZmVhAXYmZmZmYFcSFmZmZmVhAXYmYNJGmKpFsk3S9pjaSPpPZ9JK2UtDY9T0ztknSJpD5J90o6PDev2Wn6tZJmF7VOZvXkHLFOt0vRAZTJ1Hk3vKht7vR+Tq/QbjZC/cDciLhb0l7AKkkrgdOBmyNivqR5wDzgY8BxwLT0OBK4HDhS0j7AOUA3EGk+yyNie9PXyKy+nCPW0bxHzKyBImJzRNydhp8EHgAmA7OARWmyRcCJaXgWcHVkbgMmSJoEHAusjIht6YdlJTCzeWti1hjOEet03iNm1iSSpgKHAbcDXRGxOY16BOhKw5OBDbm3bUxt1doHL2MOMAegq6uL3t7eirF07ZHt7R2tavMbq507d1acdy0xNiqWIpQpFmh8PM3IkbSclsyTasaSJ7WsY5m+kyNRtjwazIWYWRNI2hO4FvhoRDwh6blxERGSoh7LiYgFwAKA7u7u6OnpqTjdpYuXccHq0af/+lMrz2+sent7qRRrEYcFXDVzfMVYilDtcylKI+NpVo6k+bVknlQzljyZO71/1OvY7PUbq7Ll0WDumjRrMEm7kv3ALI6Ib6TmLak7hfS8NbVvAqbk3n5AaqvWbtbynCPWyVyImTWQss36K4EHIuLC3KjlwMBZXbOBZbn209KZYUcBO1L3zI3ADEkT09ljM1KbWUtzjlinc9ekWWO9BfgAsFrSPantE8B8YKmkM4GHgZPSuBXA8UAf8DRwBkBEbJN0HnBnmu7TEbGtKWtg1ljOEetoLsTMGigivgeoyuijK0wfwFlV5rUQWFi/6MyK5xyxTueuSTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK8iYCjFJCyVtlXRfrm0fSSslrU3PE1O7JF0iqU/SvZIOz71ndpp+raTZlZZlZmZm1m7GukfsKmDmoLZ5wM0RMQ24Ob0GOA6Ylh5zgMshK9yAc4AjgSOAcwaKNzMzM7N2NqZCLCJuBQbfy2sWsCgNLwJOzLVfHZnbgAmSJgHHAisjYltEbAdW8uLizszMzKztNOJek10RsTkNPwJ0peHJwIbcdBtTW7X2F5E0h2xvGl1dXfT29tYvamDu9P4XtXXtUbm9WTpx+fm/686dO+v+dx6NnTt3FrZsMzNrfw296XdEhKSo4/wWAAsAuru7o6enp16zBuD0eTe8qG3u9H4uWF3cvdE7cfnrT+15bri3t5d6/51Ho8gi0MzM2l8jzprckrocSc9bU/smYEpuugNSW7V2MzMzs7bWiEJsOTBw5uNsYFmu/bR09uRRwI7UhXkjMEPSxHSQ/ozUZmZmZtbWxnr5iq8C3wdeJ2mjpDOB+cAxktYC70yvAVYA64A+4ArgrwAiYhtwHnBnenw6tZm1vCqXeDlX0iZJ96TH8blxH0+XeHlQ0rG59pmprU/SvMHLMWtVzhHrdGM6+CciTqky6ugK0wZwVpX5LAQWjiUWs5K6CvgccPWg9osi4l/yDZIOBk4GDgH2B74t6bVp9GXAMWQns9wpaXlE3N/IwM2a5CqcI9bBijsK3KwDRMStkqaOcPJZwJKIeAZ4SFIf2bX1APoiYh2ApCVpWv/IWMtzjlincyFmVoyzJZ0G3AXMTdfQmwzclpsmfymXwZd4ObLSTEd6iZdaL0vSqLNIq12mpIhLtxR9yZS8MsUCTY+nITkCrZsn1YwlT2pZxzJ9J0eibHk0mAsxs+a7nOy4yEjPFwAfrMeMR3qJl0sXL6vpsiT5S4vUU7XLlFS6pEyjXTVzfKGXTMkr+vItgzUxnoblCLRunlQzljyp5RJFzV6/sSpbHg3mQsysySJiy8CwpCuA69PLoS7l4ku8WMdwjlgnacTlK8xsCAPX2UveAwycLbYcOFnS7pIOIrsv6x1kZxNPk3SQpN3IDlZe3syYzZrJOWKdxHvEzBooXeKlB9hX0kayG9z3SDqUrNtlPfAhgIhYI2kp2QHG/cBZEfFsms/ZZNfXGwcsjIg1zV0Ts8ZwjlincyFm1kBVLvFy5RDTnw+cX6F9Bdm1+MzainPEOp27Js3MzMwK0pZ7xKYWcKaVmZmZ2Wh5j5iZmZlZQdpyj5iZWa1Wb9pR03WZ1s8/oQHRmJVPrb1OzpHKvEfMzMzMrCAuxMzMzMwK4kLMzMzMrCAuxMzMzMwK4kLMzMzMrCAuxMzMzMwK4stXWOnkT42eO71/xJcS8KnRZmbWarxHzMzMzKwgLsTMzMzMCuJCzMzMzKwgLsTMzMzMCuJCzMzMzKwgLsTMGkjSQklbJd2Xa9tH0kpJa9PzxNQuSZdI6pN0r6TDc++ZnaZfK2l2Eeti1ijOE+tkLsTMGusqYOagtnnAzRExDbg5vQY4DpiWHnOAyyH7QQLOAY4EjgDOGfhRMmsTV+E8sQ7lQsysgSLiVmDboOZZwKI0vAg4Mdd+dWRuAyZImgQcC6yMiG0RsR1YyYt/tMxalvPEOpkv6GrWfF0RsTkNPwJ0peHJwIbcdBtTW7X2F5E0h2wvAV1dXfT29lYOYI/sYrmjVW1+Y7Vz586K864lxrEq02dT7XMpSpPjcZ6M0FjypNZ1rEVR3+Wy5dFgLsTMChQRISnqOL8FwAKA7u7u6OnpqTjdpYuXccHq0af/+lMrz2+sent7qRTrSO+qUE9zp/eX5rOp9rkUpah4nCdDG0ue1Pp9r0WzP5cBZcujwdw1adZ8W1JXCul5a2rfBEzJTXdAaqvWbtbOnCfWEVyImTXfcmDgjK7ZwLJc+2nprLCjgB2pa+ZGYIakieng4xmpzaydOU+sI7hr0qyBJH0V6AH2lbSR7Kyu+cBSSWcCDwMnpclXAMcDfcDTwBkAEbFN0nnAnWm6T0fE4AObzVqW88Q6WcMKMUnrgSeBZ4H+iOhOpxd/DZgKrAdOiojtkgRcTJZcTwOnR8TdjYrNrFki4pQqo46uMG0AZ1WZz0JgYR1DMysN54l1skZ3Tb49Ig6NiO70elTXhTEzMzNrZ80+Rmy014UxMzMza1uNPEYsgJvSKcdfSKcLj/a6MJtzbSO+9ks9r4nSzGusePljW36jruNkZmbWKI0sxN4aEZskvRJYKelH+ZG1XBdmpNd+qee1h5p5jRUvf2zLb9R1nMzMzBqlYV2TEbEpPW8FriO799dorwtjZmZm1rYasqtD0njgJRHxZBqeAXya568LM58XXxfmbElLyG7YuiPXhWlmJTG1xr3N6+efUOdIzMrLeWKj0ag+py7guuyqFOwCfCUiviXpTkZxXRgzMzOzdtaQQiwi1gFvrND+GKO8LoyZtb7h9hDMnd5fyH0lzcqk1j1p1tp8ZX1rG2P5J+YuATMzK4LvNWlmZmZWEBdiZmZmZgVx16SZWR34TDmzoTlHKvMeMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzK4ik9ZJWS7pH0l2pbR9JKyWtTc8TU7skXSKpT9K9kg4vNnqz5nCeWLvzwfpmxXp7RDyaez0PuDki5kual15/DDgOmJYeRwKXp2drcUMdwDzUhW7b/QDmQZwnHazdD/L3HjGzcpkFLErDi4ATc+1XR+Y2YIKkSQXEZ1YGzhNrG94jZlacAG6SFMAXImIB0JW74f0jZPdtBZgMbMi9d2Nq25xrQ9IcYA5AV1cXvb29FRfctUe2t6UsyhRPq8RS7W/bSDt37ixiuc6TBmrndRz4uxb0vR0xF2JmxXlrRGyS9EpgpaQf5UdGRKQfnxFLP1ILALq7u6Onp6fidJcuXsYFq8uT/nOn95cmnlaJZf2pPc0NhuyHrdp3qoGcJw1Upu97vQ3kSEHf2xFz16RZQSJiU3reClwHHAFsGehKSc9b0+SbgCm5tx+Q2szamvPE2l17lsFmJSdpPPCSiHgyDc8APg0sB2YD89PzsvSW5cDZkpaQHXy8I9c1Yx2o3Q9gBueJjc1Ajgx10kslzc4RF2JmxegCrpMEWR5+JSK+JelOYKmkM4GHgZPS9CuA44E+4GngjOaHbNZ0zhNrey7EzAoQEeuAN1Zofww4ukJ7AGc1ITSz0nCeWCfwMWJmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBdml6ADMzKx5ps67oab3rZ9/Qp0jMSunWnMEassT7xEzMzMzK0hpCjFJMyU9KKlP0ryi4zErG+eI2fCcJ9ZqSlGISRoHXAYcBxwMnCLp4GKjMisP54jZ8Jwn1opKUYgBRwB9EbEuIn4FLAFmFRyTWZk4R8yG5zyxlqOIKDoGJL0XmBkRf55efwA4MiLOHjTdHGBOevk64MEmhLcv8GgTluPll3f54yNivwJjaESOFP25DlameBxLddXiObDoHIGOyJNG8Do2T8U8aamzJiNiAbCgmcuUdFdEdDdzmV5+6ZY/tajlj9ZIc6Toz3WwMsXjWKorWzy1atU8aQSvY/HK0jW5CZiSe31AajOzjHPEbHjOE2s5ZSnE7gSmSTpI0m7AycDygmMyKxPniNnwnCfWckrRNRkR/ZLOBm4ExgELI2JNwWENaGpXqJfv5VfSgBwpxXrllCkex1Jd2eJ5gQ7Ik0bwOhasFAfrm5mZmXWisnRNmpmZmXUcF2JmZmZmBenoQkzSFEm3SLpf0hpJH0nt50raJOme9Dg+956Pp1tnPCjp2DrEsF7S6rScu1LbPpJWSlqbniemdkm6JC3/XkmHj3HZr8ut4z2SnpD00Uavv6SFkrZKui/XNup1ljQ7Tb9W0uwxLPv/SfpRmv91kiak9qmSfpH7HD6fe8+b0t+tL8WnWj6LIqhJt4Cp13e7jn/nui17tH//KvGMOs+q/e2UHZx+e2r/mrID1avFUu3/XmGfT9k0K0capcjca5Sy5XRdRUTHPoBJwOFpeC/gx2S3xTgX+LsK0x8M/BDYHTgI+AkwbowxrAf2HdT2z8C8NDwP+GwaPh74JiDgKOD2On4W44BHgAMbvf7A24DDgftqXWdgH2Bdep6YhifWuOwZwC5p+LO5ZU/NTzdoPnekeJTiO67o7/Mo/s4/AV4N7Jb+ngc3aFlj/m7X+e9ct2WP9u9fJZ5R5dlQfztgKXByGv488JdDxFLt/15hn0+ZHs3MkQauQ2G518B1KlVO1/PR0XvEImJzRNydhp8EHgAmD/GWWcCSiHgmIh4C+shuqVFvs4BFaXgRcGKu/erI3AZMkDSpTss8GvhJRDw8TFxjXv+IuBXYVmHeo1nnY4GVEbEtIrYDK4GZtSw7Im6KiP708jayaw9VlZb/8oi4LbIMvjoXb9kVfQuYwv7O9Vp2LX//KvFUUy3PKv7t0pb7O4CvV1i3SrFU+79X2OdTMkXnSKM0JfcapWw5XU8dXYjlSZoKHAbcnprOTrs0Fw7s7iT7Z7Uh97aNDF24jUQAN0lapey2GwBdEbE5DT8CdDVw+QNOBr6ae92s9R8w2nVuVCwfJNsaGnCQpB9I+i9Jf5CLaWMDlt0MjfwbDlaP73Y9463Xsuv59x9NnlVrfwXweG5jYsTxDPq/V8bPpwjNzJFGKVvuNUpbfGddiAGS9gSuBT4aEU8AlwO/DRwKbAYuaODi3xoRhwPHAWdJelt+ZKrOG3qNkXQ8yR8B/56amrn+L9KMda5E0ieBfmBxatoMvCoiDgP+FviKpJc3O64WVvh3u5oil51TaJ5V+L/3nJJ8Pla70uZeo7TyOnV8ISZpV7J/Rosj4hsAEbElIp6NiN8AV/B891vdb58REZvS81bgurSsLQNdjul5a6OWnxwH3B0RW1IsTVv/nNGuc11jkXQ68C7g1JTQpK6hx9LwKrLjRl6blpPvvmyl26g07RYwdfpu1zPeei27Ln//GvKsWvtjZF0vuwxqr6rS/z1K9vkUqOVvk1TC3GuUtvjOdnQhlo6tuBJ4ICIuzLXnj7t6DzBwlsZy4GRJu0s6CJhGdoBfrcsfL2mvgWGyg8bvS8sZOJtjNrAst/zT0hkhRwE7crtlx+IUct2SzVr/QUa7zjcCMyRNTF06M1LbqEmaCfwD8EcR8XSufT9J49Lwq8nWd11a/hOSjkrfodNy8ZZdU24BU8fvdt3+zvVadr3+/jXkWcW/XdpwuAV4b4V1q7Tciv/3KNnnU6CWvk1SSXOvUdrjOxslOMOjqAfwVrJdmfcC96TH8cA1wOrUvhyYlHvPJ8n2jDzIGM+qIDsr54fpsQb4ZGp/BXAzsBb4NrBPahdwWVr+aqC7Dp/BeLIt6r1zbQ1df7KibzPwa7K++DNrWWey47n60uOMMSy7j+y4gYHvwOfTtH+S/i73AHcD787Np5vsn9tPgM+R7lLRCo/0Hf9xiv2TDVpG3b7bdfw7123Zo/37V4ln1HlW7W+XPu87Upz/Duw+RCzV/u8V9vmU7dGMHGlg7IXmXgPXq1Q5Xc+Hb3FkZmZmVpCO7po0MzMzK5ILMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLsQaQNFVSSNolvf6mpNkjfG+vpD9vbITFLlPSJyR9sVnLs3IpS35IOlXSTfWYl1kjOWdeEMPnJf3vImOoNxdiYyRpvaRfSNo58AD2z08TEcdFxKI6LOsFyZjaTpf0bG75D0n6kqTXjnV5NcTXm+J746D261J7D0BE/FNENLXYtGIUmR+p4B9Y7i8H5cmaiFgcETPGutwqsayX9M5BbadL+l4jlmfto8Nz5leS9h3U/oMU41SAiPhwRJzXiBiK4kKsPt4dEXsOPICfNXn530/L3Rt4J/ALYJWkNzQ5DoAfA6cNvJD0CuDNwM8LiMXKoZD8SAX/wDI/TMqT9DikGTE0Q37DzNpGp+bMQ8ApAy8kTQde1oTlFsqFWBPkdw1LGifpAkmPpr1XZw/eywUcKOm/JT0p6abcFsKt6fnxtIXy5vxyIuLZiPhJRPwV8F/AubkYjpL0P5Iel/TDgb1TFWL9bUnfkfRYinGxpAlp3N9LunbQ9JdIujjXtBj4U0nj0utTgOuAX+Xec66kL6fhgS2y2ZJ+mpb5yeE+U2sfzcqPCst9wR6qtJy/krQ2zfu8lA//I+kJSUsl7Zab/l2S7kk59T+SfneU6/36tO6PS1oj6Y8qfSZDxHqWpLXA2tEs11pfG+fMNeQ25IHZwNWDYrhK0mfScI+kjZLmStoqabOkM0bwEZaKC7Hm+wvgOOBQ4HDgxArTvB84A3glsBvwd6n9bel5QtpC+f4Qy/kG8AcAkiYDNwCfAfZJ87tW0n4V3ifg/5LtCn89MIXnC7ovAzNzhdkuwMm8MFF+BtwPDOy+Pm3Q+GreCrwOOBr4P5JeP4L3WPtpVn5UcyzwJuAo4B+ABcCfkeXBG0hb65IOAxYCHwJeAXwBWC5p95EsRNKuwH8CN6X1+GtgsaTXjSLWE4EjgYNH8R5rP+2UM7cBL08bKePIfl++PMzyf4usN2gycCZwmaSJNaxHYVyI1cd/pAr/cUn/Mcy0JwEXR8TGiNgOzK8wzZci4scR8QtgKVmCjdbPyIouyJJiRUSsiIjfRMRK4C7g+MFvioi+iFgZEc9ExM+BC4E/TOM2k21BvS9NPhN4NCJWDZrN1cBpkn6HLMFHktyfiohfRMQPgR8CbxzuDdYyypgf1fxzRDwREWuA+4CbImJdROwAvgkclqabA3whIm5Pe6IXAc+Q/RgNyK/348C/5cYdBewJzI+IX0XEd4DryXXLjMD/jYht6XOw9tKpOQPP7xU7BngA2DTM8n8NfDoifh0RK4CdZBv1LcOFWH2cGBET0uPEYabdH9iQe72hwjSP5IafJvuHPVqTgW1p+EDgfYN+FN4KTBr8JkldkpZI2iTpCbKtkfzBk4vICjvS8zUVlv0N4B3A2VXGV1KPdbZyKmN+VLMlN/yLCq8HlnUgMHdQTk3hhQdV59d7AvBXuXH7Axsi4je5tofJ8nakKn021h46NWcg+814P3A6I+tNeSwi+nOvW+73w4VY820GDsi9njKK98Yopn0P8N00vAG4Jv+jEBHjI6LSltM/peVMj4iXkxVbyo3/D+B3lZ0I8C6yY8JeGGTE02RbQn/JyAsxM2hefozVBuD8QTn1soj46gjf/zNgiqT8/+BX8fzW/1O88CDl36owj2aur5VXW+VMRDxMdtD+8WQb9W3PhVjzLQU+ImlyOtbqY6N478+B3wCvrjQyHbR5kKRLgR7gU2nUl4F3Szo2TfPSdJDjARVmsxfZrt0d6diyv8+PjIhfAl8HvgLcERE/rRLrJ4A/jIj1o1g/s4blR51dAXxY0pHKjJd0gqS9Rvj+28m23P9B0q7KTp55N7Akjb8H+GNJL5P0GrJjX8wqacecORN4R0Q81YS4CudCrPmuIDtA917gB8AKoB94drg3pj1N5wP/nXbtDvStv1nZtWaeAHqBlwO/FxGr0/s2ALPIiqOfk22Z/D2V//6fIjvgcwfZAf6VtkgWAdMZYm9XRPwsInzNJButRuRH3UXEXWQHSX8O2A70kXWljPT9vyIrvI4DHiU7fuy0iPhRmuQisjONt5Dl24v2PJslbZcz6ez/uxoVS9kownu3iyTpOODzEXFg0bGMlKRXAT8Cfisinig6HmtfrZgfZkVyzrQe7xFrMkl7SDpe0i6p6+8csutstYR0TMvfAktchFm9tXp+mDWbc6b1eY9Yk0l6GdnFVn+H7IySG4CPtEJRI2k8WVfJw8DM1OVpVjetnB9mRXDOtD4XYmZmZmYFcdekmZmZWUFciJmZmZkVZJfhJymnfffdN6ZOnVpx3FNPPcX48eObG9AIObbaFBnbqlWrHo2ISvflLLVWzZFqWi3mToq3VXMEWi9PHNPIlDGmqnkSES35eNOb3hTV3HLLLVXHFc2x1abI2IC7ogTf+dE+WjVHqmm1mDsp3lbNkWjBPHFMI1PGmKrlibsmzczMzAriQszMzMysIC7EzMzMzAoybCEmaaGkrZLuy7XtI2mlpLXpeWJql6RLJPVJulfS4bn3zE7Tr5U0O9f+Jkmr03sukaR6r6SZmZlZGY1kj9hVwMxBbfOAmyNiGnBzeg3ZDWynpccc4HLICjey2y4cCRwBnDNQvKVp/iL3vsHLMjMzM2tLw16+IiJulTR1UPMsoCcNLwJ6gY+l9qvT2QG3SZogaVKadmVEbAOQtBKYKakXeHlE3JbarwZOBL45lpVavWkHp8+7YdTvWz//hLEs1syqmFpDPoJz0jrLSPJk7vT+F/2+OU9aW63XEeuKiM1p+BGgKw1PBvL3H9yY2oZq31ihvSJJc8j2tNHV1UVvb2/l4PbIvqyjVW1+9bRz586mLKcWjs3MzKy5xnxB14gISU25YWVELAAWAHR3d0dPT0/F6S5dvIwLVo9+1dafWnl+9dTb20u1uIvm2MzMzJqr1rMmt6QuR9Lz1tS+CZiSm+6A1DZU+wEV2s3MzMzaXq2F2HJg4MzH2cCyXPtp6ezJo4AdqQvzRmCGpInpIP0ZwI1p3BOSjkpnS56Wm5eZmZlZWxu2/07SV8kOtt9X0kaysx/nA0slnQk8DJyUJl8BHA/0AU8DZwBExDZJ5wF3puk+PXDgPvBXZGdm7kF2kP6YDtQ3MzMzaxUjOWvylCqjjq4wbQBnVZnPQmBhhfa7gDcMF4eZmZlZu/GV9c0aSNJLJd0h6YeS1kj6VGo/SNLt6ULGX5O0W2rfPb3uS+On5ub18dT+oKRjC1ols7pyjlincyFm1ljPAO+IiDcCh5JdP+8o4LPARRHxGmA7cGaa/kxge2q/KE2HpIOBk4FDyC56/G+SxjVzRcwaxDliHc2FmFkDRWZnerlregTwDuDrqX0R2YWMIbso8qI0/HXg6HQiyyxgSUQ8ExEPkR2HeUTj18CssZwj1unGfB0xMxta2ipfBbwGuAz4CfB4RAxcdTh/IePnLn4cEf2SdgCvSO235WZb8eLHI73ocbMvkFvLBZbhhRdZbrWL+jrekWtmjqTltWyeVLpgedHfszJ+18sYUzUuxMwaLCKeBQ6VNAG4DvidBi5rRBc9bvYFcmu55Ri88CLLrXZRX8c7cs3MkbS8ls2TudP7X3TB8mZcjHwoZfyulzGmatw1adYkEfE4cAvwZmCCpIH/pvkLGT938eM0fm/gMapfFNmsbThHrBO5EDNrIEn7pa18JO0BHAM8QPZj89402eCLIg9cLPm9wHfSZWGWAyenM8YOAqYBdzRlJcwayDlinc5dk2aNNQlYlI6BeQmwNCKul3Q/sETSZ4AfAFem6a8ErpHUB2wjOwuMiFgjaSlwP9APnJW6c8xanXPEOpoLMbMGioh7gcMqtK+jwhldEfFL4H1V5nU+cH69YzQrknPEOp0LMbMOtHrTjpoOoF8//4QGRGNWTs4TawYfI2ZmZmZWEBdiZmZmZgVxIWZmZmZWEBdiZmZmZgVxIWZmZmZWEBdiZmZmZgVxIWZmZmZWEBdiZmZmZgVxIWZmZmZWkDEVYpL+l6Q1ku6T9FVJL5V0kKTbJfVJ+pqk3dK0u6fXfWn81Nx8Pp7aH5R07BjXyczMzKwl1FyISZoM/A3QHRFvAMaR3Xz1s8BFEfEaYDtwZnrLmcD21H5Rmg5JB6f3HQLMBP4t3fzVzMzMrK2NtWtyF2APSbsALwM2A+8Avp7GLwJOTMOz0mvS+KMlKbUviYhnIuIhoI8KN3o1MzMzazc13/Q7IjZJ+hfgp8AvgJuAVcDjEdGfJtsITE7Dk4EN6b39knYAr0jtt+VmnX/PC0iaA8wB6Orqore3t2JsXXvA3On9FccNpdr86mnnzp1NWU4tHFv9SZoCXA10AQEsiIiLJZ0L/AXw8zTpJyJiRXrPx8n2ID8L/E1E3JjaZwIXk+19/mJEzG/muhRhau6Gy3On94/4Bsy+6XLrcI6M3dQabkwOzpOyqLkQkzSRbG/WQcDjwL+TdS02TEQsABYAdHd3R09PT8XpLl28jAtWj37V1p9aeX711NvbS7W4i+bYGqIfmBsRd0vaC1glaWUad1FE/Et+4kFd9fsD35b02jT6MuAYso2VOyUtj4j7m7IWZo3jHLGOVnMhBrwTeCgifg4g6RvAW4AJknZJe8UOADal6TcBU4CNqStzb+CxXPuA/HvMWlpEbCbrsicinpT0AFX2+CbPddUDD0nKd9X3RcQ6AElL0rT+kbGW5hyxTjeWQuynwFGSXkbWNXk0cBdwC/BeYAkwG1iWpl+eXn8/jf9ORISk5cBXJF1ItnUzDbhjDHGZlVI6U/gw4HayjZazJZ1GljdzI2I7Q3fVbxjUfmSFZZSy+76WZQ02mpjL0I3dat3pZYi3GTmSltOyeVJrTJXU6+9dhu/OYGWMqZqxHCN2u6SvA3eT7Vr+AVm34Q3AEkmfSW1XprdcCVyTtl62ke1aJiLWSFpKttXSD5wVEc/WGpdZGUnaE7gW+GhEPCHpcuA8smNizgMuAD441uWUtft+pMd2DWXu9P4Rx9yMwwyG02rd6UXH26wcgdbOk9HkwXDqlSdFf3cqKWNM1YzprxkR5wDnDGpeR4WzHiPil8D7qsznfOD8scRiVlaSdiX7gVkcEd8AiIgtufFXANenl0N11bsL39qSc8Q6ma+sb9ZA6RItVwIPRMSFufZJucneA9yXhpcDJ6cLIB/E8131dwLT0gWTdyPbo7y8Getg1kjOEet09dm/aWbVvAX4ALBa0j2p7RPAKZIOJet2WQ98CIbuqpd0NnAj2an5CyNiTfNWw6xhnCPW0VyImTVQRHwPUIVRK4Z4T8Wu+nQNparvM2tFzhHrdO6aNDMzMyuICzEzMzOzgrgQMzMzMyuICzEzMzOzgrgQMzMzMyuICzEzMzOzgrgQMzMzMyuICzEzMzOzgrgQMzMzMyuICzEzMzOzgrgQMzMzMyuICzEzMzOzgrgQMzMzMyuICzGzBpI0RdItku6XtEbSR1L7PpJWSlqbniemdkm6RFKfpHslHZ6b1+w0/VpJs4taJ7N6co5Yp3MhZtZY/cDciDgYOAo4S9LBwDzg5oiYBtycXgMcB0xLjznA5ZD9KAHnAEcCRwDnDPwwmbU454h1NBdiZg0UEZsj4u40/CTwADAZmAUsSpMtAk5Mw7OAqyNzGzBB0iTgWGBlRGyLiO3ASmBm89bErDGcI9bpdik6ALNOIWkqcBhwO9AVEZvTqEeArjQ8GdiQe9vG1FatffAy5pDtJaCrq4ve3t6KsXTtAXOn9496HarNbzi1LGuw0cRca5z1tHPnzlLEMVJliLcZOZKW07J5UmtMldTr712G785gZYypmjEVYpImAF8E3gAE8EHgQeBrwFRgPXBSRGyXJOBi4HjgaeD0ga2g1Jf/j2m2n4mIRZi1EUl7AtcCH42IJ7J0yERESIp6LCciFgALALq7u6Onp6fidJcuXsYFq0ef/utPrTy/4Zw+74aa3pc3d3r/iGOuNc566u3tpdrnX0ZFx9usHEnza9k8GU0eDKdeeVL0d6eSMsZUzVi7Ji8GvhURvwO8kWyXsvv1zXIk7Ur2A7M4Ir6Rmrek7hTS89bUvgmYknv7AamtWrtZy3OOWCeruRCTtDfwNuBKgIj4VUQ8jvv1zZ6T9gRfCTwQERfmRi0HBs7qmg0sy7Wfls4MOwrYkbpnbgRmSJqYNlRmpDazluYcsU43lv2bBwE/B74k6Y3AKuAjdGC//miUud/asTXEW4APAKsl3ZPaPgHMB5ZKOhN4GDgpjVtB1n3fR9aFfwZARGyTdB5wZ5ru0xGxrSlrYNZYzhHraGMpxHYBDgf+OiJul3Qxz3dDAp3Trz8aZe63dmz1FxHfA1Rl9NEVpg/grCrzWggsrF90ZsVzjlinG8sxYhuBjRFxe3r9dbLCzP36ZmZmZiNQcyEWEY8AGyS9LjUdDdyP+/XNzMzMRmSs58D+NbBY0m7AOrK++pfgfn0zMzOzYY2pEIuIe4DuCqPcr29mZmY2DN/iyMzMzKwgLsTMzMzMCuJCzMzMzKwgLsTMzMzMCuJCzMzMzKwgLsTMzMzMCuJCzMzMzKwgY72gq5l1kKnzbig6BLPSc57YaHiPmJmZmVlBXIiZmZmZFcSFmFkDSVooaauk+3Jt50raJOme9Dg+N+7jkvokPSjp2Fz7zNTWJ2les9fDrFGcI9bpfIyYWWNdBXwOuHpQ+0UR8S/5BkkHAycDhwD7A9+W9No0+jLgGGAjcKek5RFxfyMDb2VjOUZn/fwT6hiJjcBVOEcKUWueOEfqy4WYWQNFxK2Spo5w8lnAkoh4BnhIUh9wRBrXFxHrACQtSdP6R8ZannPEOp0LMbNinC3pNOAuYG5EbAcmA7flptmY2gA2DGo/stJMJc0B5gB0dXXR29tbceFde8Dc6f1jib/pmhVztc9stHbu3Fm3eTVDCeNtSI5Aa+dJGWIa/HmV8LtTypiqcSFm1nyXA+cBkZ4vAD5YjxlHxAJgAUB3d3f09PRUnO7Sxcu4YHVrpf/c6f1NiXn9qT11mU9vby/VPv8yKlm8DcsRaO08aVYeDGVwjpTsuwOUM6ZqyvUNM+sAEbFlYFjSFcD16eUmYEpu0gNSG0O0m7Ud54h1Ep81adZkkiblXr4HGDhbbDlwsqTdJR0ETAPuAO4Epkk6SNJuZAcrL29mzGbN5ByxTuI9YmYNJOmrQA+wr6SNwDlAj6RDybpd1gMfAoiINZKWkh1g3A+cFRHPpvmcDdwIjAMWRsSa5q6JWWM4R6zTuRAza6CIOKVC85VDTH8+cH6F9hXAijqGZlYKzhHrdGPumpQ0TtIPJF2fXh8k6fZ0Ub2vpd3EpF3JX0vtt+dPV652gT4zMzOzdlaPY8Q+AjyQe/1ZsgvxvQbYDpyZ2s8Etqf2i9J0gy/QNxP4N0nj6hCXmZmZWamNqRCTdABwAvDF9FrAO4Cvp0kWASem4VnpNWn80Wn65y7QFxEPAfkL9JmZmZm1rbEeI/avwD8Ae6XXrwAej4iBq83lL7Y3mXTBvYjol7QjTT/UBfpeoNEX4WvGxd/KfJE5x2ZmZtZcNRdikt4FbI2IVZJ66hbREBp9Eb56XchxKGW+yJxjMzMza66x7BF7C/BHko4HXgq8HLgYmCBpl7RXLH9RvYEL8W2UtAuwN/AYQ1+gz8zMzKxt1XyMWER8PCIOiIipZAfbfyciTgVuAd6bJpsNLEvDy9Nr0vjvRERQ/QJ9ZmZmZm2tEdcR+xiwRNJngB/w/PVgrgSukdQHbCMr3oa8QJ+ZmZlZO6tLIRYRvUBvGl5HhbMeI+KXwPuqvL/iBfrMzMzM2pnvNWlmZmZWEBdiZmZmZgVxIWZmZmZWEBdiZmZmZgVxIWbWQJIWStoq6b5c2z6SVkpam54npnZJukRSn6R7JR2ee8/sNP1aSbMrLcusVTlPrJO5EDNrrKvIbmafNw+4OSKmATen1wDHkV1HbxrZrbwuh+wHCTgHOJLsjORzBn6UzNrEVThPrEO5EDNroIi4ley6eXmzgEVpeBFwYq796sjcRnaXiknAscDKiNgWEduBlbz4R8usZTlPrJM14oKuZja0rojYnIYfAbrS8GRgQ266jamtWvuLSJpDtpeArq6uqjdK79oD5k7vrzH8YjQr5nrdXL7VblRfwnidJxWUIabBn1cJvzuljKkaF2JmBYqIkBR1nN8CYAFAd3d3VLtR+qWLl3HB6tZK/7nT+5sS8/pTe+oyn1a7UX2Z43WePK9ZeTCUwTlSxu9OGWOqxl2TZs23JXWlkJ63pvZNwJTcdAektmrtZu3MeWIdwYWYWfMtBwbO6JoNLMu1n5bOCjsK2JG6Zm4EZkiamA4+npHazNqZ88Q6Qrn2uZq1GUlfBXqAfSVtJDuraz6wVNKZwMPASWnyFcDxQB/wNHAGQERsk3QecGea7tMRMfjAZrOW5TyxTuZCzKyBIuKUKqOOrjBtAGdVmc9CYGEdQzMrDeeJdTJ3TZqZmZkVxIWYmZmZWUFciJmZmZkVxIWYmZmZWUFciJmZmZkVxIWYmZmZWUF8+Qozs5yp826o6X3r559Q50jMymlwjsyd3s/pI8gb50hlNe8RkzRF0i2S7pe0RtJHUvs+klZKWpueJ6Z2SbpEUp+keyUdnpvX7DT9Wkmzqy3TzMzMrJ2MpWuyH5gbEQcDRwFnSToYmAfcHBHTgJvTa4DjgGnpMQe4HLLCjewqykcCRwDnDBRvZmZmZu2s5kIsIjZHxN1p+EngAWAyMAtYlCZbBJyYhmcBV0fmNmBCupHrscDKiNgWEduBlcDMWuMyMzMzaxV1OUZM0lTgMOB2oCvdgBXgEaArDU8GNuTetjG1VWuvtJw5ZHvT6Orqore3t2I8XXtkfdajVW1+9bRz586mLKcWjs3MzKy5xlyISdoTuBb4aEQ8Iem5cRERkmKsy8jNbwGwAKC7uzt6enoqTnfp4mVcsHr0q7b+1Mrzq6fe3l6qxV00x2ZmZtZcY7p8haRdyYqwxRHxjdS8JXU5kp63pvZNwJTc2w9IbdXazczMzNraWM6aFHAl8EBEXJgbtRwYOPNxNrAs135aOnvyKGBH6sK8EZghaWI6SH9GajNra5LWS1ot6R5Jd6W2UZ91bNbOnCfW7sayR+wtwAeAd6QEuUfS8cB84BhJa4F3ptcAK4B1QB9wBfBXABGxDTgPuDM9Pp3azDrB2yPi0IjoTq9HddaxWYdwnljbqvkYsYj4HqAqo4+uMH0AZ1WZ10JgYa2xmLWRWUBPGl4E9AIfI3fWMXCbpAmSJuVOjDHrJM4Taxu+sr5ZcQK4KZ3Q8oV0Mspozzp+wQ9Mo88sLlLZYx78Wbfamb4ljtd5ktPKMTXz+1Xi7/OLuBAzK85bI2KTpFcCKyX9KD+ylrOOG31mcZHmTu8vdcyDz7putTN9Sxyv8ySnjHkw0piacWWCASX+Pr+Ib/ptVpCI2JSetwLXkd1ZYrRnHZu1NeeJtTsXYmYFkDRe0l4Dw2RnC9/H6M86NmtbzhPrBOXav2nWObqA69IFkHcBvhIR35J0J7BU0pnAw8BJafoVwPFkZx0/DZzR/JDNms55Ym3PhZhZASJiHfDGCu2PMcqzjs3alfPEOoG7Js3MzMwK4kLMzMzMrCAuxMzMzMwK4mPEzMzqYOq8G17weu70fk4f1FbJ+vknNCoks1IZnCMj1e454j1iZmZmZgVxIWZmZmZWEBdiZmZmZgVxIWZmZmZWEBdiZmZmZgXxWZNmZgXymWRmQ6slR+ZO76en/qE0hAsxaxj/wJiZmQ3NhVgdjKbgyF9bqNkFx3BxVrvuUasURrUWftA662hmZu3FhZgNaywFjpmZmVVXmkJM0kzgYmAc8MWImN/sGJpdcLjAqazS5zLSq5S3szLkiFnZOU+s1ZSiEJM0DrgMOAbYCNwpaXlE3F9sZGbl4ByxwXwM5os5TyyvVXKkLJevOALoi4h1EfErYAkwq+CYzMrEOWI2POeJtZxS7BEDJgMbcq83AkcWFItZGTlHrC6q7SUYrvu/RfakOU9szJp94ldZCrERkTQHmJNe7pT0YJVJ9wUebU5Uo/M3jq0mjY5Nnx1y9IGNWm69tUOOVFPm72cl7RZvu+QItHaelPF75ZieV0uelKUQ2wRMyb0+ILW9QEQsABYMNzNJd0VEd/3Cqx/HVpsyx9YkHZMj1bRazI63EG2fJ45pZMoYUzVlOUbsTmCapIMk7QacDCwvOCazMnGOmA3PeWItpxR7xCKiX9LZwI1kpxwvjIg1BYdlVhrOEbPhOU+sFZWiEAOIiBXAijrNbthdzgVybLUpc2xN0UE5Uk2rxex4C9ABeeKYRqaMMVWkiCg6BjMzM7OOVJZjxMzMzMw6TlsVYpJmSnpQUp+keQUsf6GkrZLuy7XtI2mlpLXpeWJql6RLUqz3Sjq8wbFNkXSLpPslrZH0kbLEJ+mlku6Q9MMU26dS+0GSbk8xfC0dfIuk3dPrvjR+aqNia0dF58lwRpNHZTDa3CqD0eZcpykyRyStl7Ra0j2S7kptTf0/Xa/fMkmz0/RrJc1uQEznStqUPqt7JB2fG/fxFNODko7NtZfv/19EtMWD7MDMnwCvBnYDfggc3OQY3gYcDtyXa/tnYF4angd8Ng0fD3wTEHAUcHuDY5sEHJ6G9wJ+DBxchvjSMvZMw7sCt6dlLgVOTu2fB/4yDf8V8Pk0fDLwtaK/f63yKEOejCDGEedRGR6jza0yPEabc530KDpHgPXAvoPamvp/uh6/ZcA+wLr0PDENT6xzTOcCf1dh2oPT32134KD09xxX9N+22qOd9ogVfmuLiLgV2DaoeRawKA0vAk7MtV8dmduACZImNTC2zRFxdxp+EniA7CrUhceXlrEzvdw1PQJ4B/D1KrENxPx14GhJakRsbajwPBnOKPOocDXkVuFqyLlOUsYcaer/6Tr9lh0LrIyIbRGxHVgJzKxzTNXMApZExDMR8RDQR/Z3LePftq0KsUq3tphcUCx5XRGxOQ0/AnSl4cLiTV15h5FtBZciPknjJN0DbCVL2J8Aj0dEf4XlPxdbGr8DeEWjYmszZc2T4VT7npbKCHOrFEaZc52k6BwJ4CZJq5TdAQDK8X96tDE0K7azU5fowlz3f9ExjUo7FWKlF9k+00JPU5W0J3At8NGIeCI/rsj4IuLZiDiU7ErYRwC/U0QcVn5lyKNKyppb1TjnSuutEXE4cBxwlqS35UeW4btUhhiSy4HfBg4FNgMXFBpNjdqpEBvRrS0KsGVgV3F63pramx6vpF3JfigWR8Q3yhYfQEQ8DtwCvJlsF/fAte7yy38utjR+b+CxRsfWJsqaJ8Op9j0thVHmVqmMMOc6SaE5EhGb0vNW4DqyIrkM/6dHG0PDY4uILWmD4jfAFWSfVaEx1aKdCrGy3tpiOTBwtshsYFmu/bR0xslRwI7cbt+6S8dQXQk8EBEXlik+SftJmpCG9wCOITvO5hbgvVViG4j5vcB30haaDa+seTKcat/TwtWQW4WrIec6SWE5Imm8pL0GhoEZwH2U4P90DTHcCMyQNDF1Gc5IbXUz6Hi495B9VgMxnazsDPuDgGnAHZT1/1+RZwrU+0F29saPyY51+GQBy/8q2e7RX5P1PZ9JduzSzcBa4NvAPmlaAZelWFcD3Q2O7a1ku5LvBe5Jj+PLEB/wu8APUmz3Af8ntb+aLHn6gH8Hdk/tL02v+9L4Vxf93WulR9F5MoL4RpxHZXiMNrfK8BhtznXao6gcSZ//D9NjzcCym/1/ul6/ZcAH03epDzijATFdk5Z5L1lBNSk3/SdTTA8CxxX9tx3q4Svrm5mZmRWknbomzczMzFqKCzEzMzOzgrgQMzMzMyuICzEzMzOzgrgQMzMzMyuICzEzMzOzgrgQMzMzMyuICzEzMzOzgrgQMzMzMyuICzEzMzOzgrgQMzMzMyuICzEzMzOzgrgQMzMzMyuICzEzMzOzgrgQK4ikqyR9pknL+ktJWyTtlPSKJi1zqqSQtEszlmdmZtaKXIi1OEm/L+k7kp6UtEPSf0o6ODd+V+BCYEZE7An8k6TL8+MlPVWl7aimroyZmVmHcSHWwiS9GbgJWAbsDxwE/BD4b0mvTpN1AS8F1qTXtwJvy82mG/gp8AeD2gBWNSZyMzMzAxdiTSPpMEl3pz1XXyMrjpA0UdL1kn4uaXsaPiCNe5+kVYPm87eSlqWX/wxcHREXR8STEbEtIv4RuA04V9JrgQfTtI9L+g5ZIfZ6Sfum9j8AlgDjB7V9PyJ+LWl/Sdem+B6S9De5WF4iaZ6kn0h6TNJSSftUWf8/kbRe0hvG9kmamZm1DxdiTSBpN+A/gGuAfYB/B/4kjX4J8CXgQOBVwC+Az6Vxy4GDJL0+N7sPAFdLehnw+2legy0FjomIHwOHpLYJEfGOiNgAPMzze8DeBnwX+J9BbbdKegnwn2R72SYDRwMflXRsmu6vgROBPyTbI7cduKzC+p8BfBZ4Z0TcV+1zMjMz6zQuxJrjKGBX4F8j4tcR8XXgToCIeCwiro2IpyPiSeB8ssKGiHgG+BrwZwCSDgGmAteTFXQvATZXWN5mYN8K7QP+C3hbKrSOINuD9t1c21vSNL8H7BcRn46IX0XEOuAK4OQ0nw8Dn4yIjSnWc4H3DjpA/6PA3wM9EdE3kg/LzMysU7gQa479gU0REbm2hwEkvUzSFyQ9LOkJsq7DCZLGpekWAe+XJLK9YUtT0bMd+A0wqcLyJgGPDhHPwHFi04F1EfE08L1c2x7A7WR76faX9PjAA/gE2XFnpPHX5cY9ADybGw9ZEXZZRGwc8hMyMzPrQC7EmmMzMDkVUwNelZ7nAq8DjoyIl/P8gfQCiIjbgF+RdRu+n6x7k4h4Cvg+8L4KyzsJuHmIeG4F3gicQLYnDLKD+aektjsj4pfABuChiJiQe+wVEcen92wAjhs0/qURsSm3rBnAP0r6E8zMzOwFXIg1x/eBfuBv0qUh/pisSxBgL7Ljwh5PB7qfU+H9V5MdN/briPhern0eMFvS30jaKx34/xngzcCnqgWTugi3AB8hFWJpb93tqe3WNOkdwJOSPiZpD0njJL1B0u+l8Z8Hzpd0IICk/STNGrS4NcBM4DJJfzTkp2RmZtZhXIg1QUT8Cvhj4HRgG/CnwDfS6H8l6wp8lOxYrW9VmMU1wBuALw+a7/eAY9O8N5N1dx4GvDUi1g4T1q3AfsB/59q+C7wyjSMingXeBRwKPJRi/CKwd5r+YrITCm6S9GSK/8gK6//DNJ8rJB03TFxmZmYdQy88bMnKSNIewFbg8BEUWGZmZtYivEesNfwl2XFbLsLMzMzaiO8DWHKS1pMduH9isZGYmZlZvblr0szMzKwg7po0MzMzK0jLdk3uu+++MXXq1IrjnnrqKcaPH9/cgOrEsRdjqNhXrVr1aETs1+SQzMysA7RsITZ16lTuuuuuiuN6e3vp6elpbkB14tiLMVTskh5ubjRmZtYp3DVpZmZmVhAXYmZmZmYFcSFmZmZmVhAXYmZmZmYFqbkQkzRF0i2S7pe0RtJHUvs+klZKWpueJ6Z2SbpEUp+keyUdnpvX7DT9Wkmzx75aZmZmZuU3lrMm+4G5EXG3pL2AVZJWkt3Y+uaImC9pHjAP+BhwHDAtPY4ELgeOlLQPcA7QDUSaz/KI2F5rYKs37eD0eTeM+n3r559Q6yLNzMzMRq3mPWIRsTki7k7DTwIPAJOBWcCiNNkinr81zyzg6sjcBkyQNAk4FlgZEdtS8bUSmFlrXGZmZmatoi7XEZM0FTgMuB3oiojNadQjQFcangxsyL1tY2qr1l5pOXOAOQBdXV309vZWjKdrD5g7vX/U61Ftfs20c+fOUsSRt3rTjhFN17UHXLp42XOvp0/eu1Eh1V0ZP3czM2t/Yy7EJO0JXAt8NCKekPTcuIgISXW7mWVELAAWAHR3d0e1C3BeungZF6we/aqtP7Xy/JqpjBdFHWk379zp/S/43MvweY5UGT93MzNrf2M6a1LSrmRF2OKI+EZq3pK6HEnPW1P7JmBK7u0HpLZq7WZmZmZtbSxnTQq4EnggIi7MjVoODJz5OBtYlms/LZ09eRSwI3Vh3gjMkDQxnWE5I7WZmZmZtbWxdE2+BfgAsFrSPantE8B8YKmkM4GHgZPSuBXA8UAf8DRwBkBEbJN0HnBnmu7TEbFtDHG1jKkVuvzmTu8ftivQZ3eamZm1h5oLsYj4HqAqo4+uMH0AZ1WZ10JgYa2x1EulwmgkXBiZmZlZLepy1qS1hloLzWZzQWxmZp3CtzgyMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OC1FyISVooaauk+3Jt50raJOme9Dg+N+7jkvokPSjp2Fz7zNTWJ2le7atiZmZm1lrGskfsKmBmhfaLIuLQ9FgBIOlg4GTgkPSef5M0TtI44DLgOOBg4JQ0rZmZmVnb26XWN0bErZKmjnDyWcCSiHgGeEhSH3BEGtcXEesAJC1J095fa1xmZmZmraLmQmwIZ0s6DbgLmBsR24HJwG25aTamNoANg9qPrDZjSXOAOQBdXV309vZWnK5rD5g7vb/W+EetWhzDqRTjSGKv5/LqaXDszY6z1uUB7Ny5c0zvNzMzq0W9C7HLgfOASM8XAB+s18wjYgGwAKC7uzt6enoqTnfp4mVcsLoRNWZl60+tHMdwTp93w4va5k7vHzb2ei6vngbH3uw4a10eZEVcte+TmZlZo9S1WomILQPDkq4Ark8vNwFTcpMekNoYot3MzMysrdX18hWSJuVevgcYOKNyOXCypN0lHQRMA+4A7gSmSTpI0m5kB/Qvr2dMZmZmZmVV8x4xSV8FeoB9JW0EzgF6JB1K1jW5HvgQQESskbSU7CD8fuCsiHg2zeds4EZgHLAwItbUGpOZmZlZKxnLWZOnVGi+cojpzwfOr9C+AlhRaxxmZmZmrcpX1jczMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4K4EDMzMzMrSPNuyGjWYFPHcC/Nq2aOr2MkZmZmI+M9YmZmZmYFcSFmZmZmVhAXYmZmZmYFcSFmZmZmVhAXYmZmZmYFcSFmZmZmVhAXYmZmZmYFcSFmZmZmVhAXYmZmZmYFcSFmZmZmVhAXYmZmZmYFcSFmZmZmVhAXYmZmZmYFcSFmZmZmVhAXYmZmZmYFcSFmZmZmVhAXYmZmZmYFcSFmZmZmVpAxFWKSFkraKum+XNs+klZKWpueJ6Z2SbpEUp+keyUdnnvP7DT9WkmzxxKTmZmZWasY6x6xq4CZg9rmATdHxDTg5vQa4DhgWnrMAS6HrHADzgGOBI4Azhko3szMzMza2ZgKsYi4Fdg2qHkWsCgNLwJOzLVfHZnbgAmSJgHHAisjYltEbAdW8uLizszMzKzt7NKAeXZFxOY0/AjQlYYnAxty021MbdXaX0TSHLK9aXR1ddHb21s5gD1g7vT+GsMfvWpxDKdSjCOJvZ7Lq6fBsZc1zkp27txZc7xmZma1akQh9pyICElRx/ktABYAdHd3R09PT8XpLl28jAtWN3TVXmD9qZXjGM7p8254Udvc6f3Dxl7P5dXT4NjLGmclV80cT7Xvk5mZWaM04qzJLanLkfS8NbVvAqbkpjsgtVVrNzMzM2trjSjElgMDZz7OBpbl2k9LZ08eBexIXZg3AjMkTUwH6c9IbWZmZmZtbUz9d5K+CvQA+0raSHb243xgqaQzgYeBk9LkK4DjgT7gaeAMgIjYJuk84M403acjYvAJAGZmZmZtZ0yFWEScUmXU0RWmDeCsKvNZCCwcSyxmZmZmrcZX1jczMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4I0rBCTtF7Sakn3SLorte0jaaWktel5YmqXpEsk9Um6V9LhjYrLzMzMrCwavUfs7RFxaER0p9fzgJsjYhpwc3oNcBwwLT3mAJc3OC4zMzOzwjW7a3IWsCgNLwJOzLVfHZnbgAmSJjU5NjMzM7OmUkQ0ZsbSQ8B2IIAvRMQCSY9HxIQ0XsD2iJgg6XpgfkR8L427GfhYRNw1aJ5zyPaY0dXV9aYlS5ZUXPbWbTvY8ouGrFZF0yfvXdP7Vm/a8aK2rj0YNvZ6Lq+eBsde1jgrOWjvcey5554Vx7397W9fldura2ZmVje7NHDeb42ITZJeCayU9KP8yIgISaOqAiNiAbAAoLu7O3p6eipOd+niZVywupGr9kLrT60cx3BOn3fDi9rmTu8fNvZ6Lq+eBsde1jgruWrmeKp9n8zMzBqlYV2TEbEpPW8FrgOOALYMdDmm561p8k3AlNzbD0htZmZmZm2rIYWYpPGS9hoYBmYA9wHLgdlpstnAsjS8HDgtnT15FLAjIjY3IjYzMzOzsmhU/10XcF12GBi7AF+JiG9JuhNYKulM4GHgpDT9CuB4oA94GjijQXGZmZmZlUZDCrGIWAe8sUL7Y8DRFdoDOKsRsZiZmZmVla+sb2ZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlaQ0hRikmZKelBSn6R5RcdjZmZm1milKMQkjQMuA44DDgZOkXRwsVGZmZmZNVYpCjHgCKAvItZFxK+AJcCsgmMyMzMzayhFRNExIOm9wMyI+PP0+gPAkRFx9qDp5gBz0svXAQ9WmeW+wKMNCrfRHHsxhor9wIjYr5nBmJlZZ9il6ABGIyIWAAuGm07SXRH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+ "text/plain": [ + "
" + ] }, "metadata": { "needs_background": "light" diff --git a/tests/notebook/test_etl.ipynb b/tests/notebook/test_etl.ipynb index 5f16c20..6d2474e 100644 --- a/tests/notebook/test_etl.ipynb +++ b/tests/notebook/test_etl.ipynb @@ -18,7 +18,9 @@ "outputs": [ { "data": { - "text/plain": "False" + "text/plain": [ + "False" + ] }, "execution_count": 2, "metadata": {}, @@ -27,7 +29,7 @@ ], "source": [ "es = Elasticsearch()\n", - "ed_df = ed.DataFrame('localhost', 'flights', columns = [\"AvgTicketPrice\", \"Cancelled\", \"dayOfWeek\", \"timestamp\", \"DestCountry\"])\n", + "ed_df = ed.DataFrame('http://localhost:9200', 'flights', columns = [\"AvgTicketPrice\", \"Cancelled\", \"dayOfWeek\", \"timestamp\", \"DestCountry\"])\n", "es.indices.exists(index=\"churn\")" ] }, @@ -57,7 +59,9 @@ "outputs": [ { "data": { - "text/plain": "pandas.core.frame.DataFrame" + "text/plain": [ + "pandas.core.frame.DataFrame" + ] }, "execution_count": 4, "metadata": {}, @@ -75,8 +79,125 @@ "outputs": [ { "data": { - "text/plain": " account length area code churn customer service calls \\\n0 128 415 0 1 \n1 107 415 0 1 \n\n international plan number vmail messages phone number state \\\n0 no 25 382-4657 KS \n1 no 26 371-7191 OH \n\n total day calls total day charge ... total eve calls total eve charge \\\n0 110 45.07 ... 99 16.78 \n1 123 27.47 ... 103 16.62 \n\n total eve minutes total intl calls total intl charge total intl minutes \\\n0 197.4 3 2.7 10.0 \n1 195.5 3 3.7 13.7 \n\n total night calls total night charge total night minutes voice mail plan \n0 91 11.01 244.7 yes \n1 103 11.45 254.4 yes \n\n[2 rows x 21 columns]", - "text/html": "
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account lengtharea codechurncustomer service callsinternational plannumber vmail messagesphone numberstatetotal day callstotal day charge...total eve callstotal eve chargetotal eve minutestotal intl callstotal intl chargetotal intl minutestotal night callstotal night chargetotal night minutesvoice mail plan
012841501no25382-4657KS11045.07...9916.78197.432.710.09111.01244.7yes
110741501no26371-7191OH12327.47...10316.62195.533.713.710311.45254.4yes
\n
\n

2 rows × 21 columns

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account lengtharea codechurncustomer service callsinternational plannumber vmail messagesphone numberstatetotal day callstotal day charge...total eve callstotal eve chargetotal eve minutestotal intl callstotal intl chargetotal intl minutestotal night callstotal night chargetotal night minutesvoice mail plan
012841501no25382-4657KS11045.07...9916.78197.432.710.09111.01244.7yes
110741501no26371-7191OH12327.47...10316.62195.533.713.710311.45254.4yes
\n", + "
\n", + "

2 rows × 21 columns

" + ], + "text/plain": [ + " account length area code churn customer service calls \\\n", + "0 128 415 0 1 \n", + "1 107 415 0 1 \n", + "\n", + " international plan number vmail messages phone number state \\\n", + "0 no 25 382-4657 KS \n", + "1 no 26 371-7191 OH \n", + "\n", + " total day calls total day charge ... total eve calls total eve charge \\\n", + "0 110 45.07 ... 99 16.78 \n", + "1 123 27.47 ... 103 16.62 \n", + "\n", + " total eve minutes total intl calls total intl charge total intl minutes \\\n", + "0 197.4 3 2.7 10.0 \n", + "1 195.5 3 3.7 13.7 \n", + "\n", + " total night calls total night charge total night minutes voice mail plan \n", + "0 91 11.01 244.7 yes \n", + "1 103 11.45 254.4 yes \n", + "\n", + "[2 rows x 21 columns]" + ] }, "execution_count": 5, "metadata": {}, @@ -85,7 +206,7 @@ ], "source": [ "# NBVAL_IGNORE_OUTPUT\n", - "ed.csv_to_eland(\"./test_churn.csv\", es_client='localhost', es_dest_index='churn', es_refresh=True, index_col=0)" + "ed.csv_to_eland(\"./test_churn.csv\", es_client='http://localhost:9200', es_dest_index='churn', es_refresh=True, index_col=0)" ] }, { @@ -95,7 +216,37 @@ "outputs": [ { "data": { - "text/plain": "{'took': 0,\n 'timed_out': False,\n '_shards': {'total': 1, 'successful': 1, 'skipped': 0, 'failed': 0},\n 'hits': {'total': {'value': 2, 'relation': 'eq'},\n 'max_score': 1.0,\n 'hits': [{'_index': 'churn',\n '_id': '0',\n '_score': 1.0,\n '_source': {'state': 'KS',\n 'account length': 128,\n 'area code': 415,\n 'phone number': '382-4657',\n 'international plan': 'no',\n 'voice mail plan': 'yes',\n 'number vmail messages': 25,\n 'total day minutes': 265.1,\n 'total day calls': 110,\n 'total day charge': 45.07,\n 'total eve minutes': 197.4,\n 'total eve calls': 99,\n 'total eve charge': 16.78,\n 'total night minutes': 244.7,\n 'total night calls': 91,\n 'total night charge': 11.01,\n 'total intl minutes': 10.0,\n 'total intl calls': 3,\n 'total intl charge': 2.7,\n 'customer service calls': 1,\n 'churn': 0}}]}}" + "text/plain": [ + "{'took': 0,\n", + " 'timed_out': False,\n", + " '_shards': {'total': 1, 'successful': 1, 'skipped': 0, 'failed': 0},\n", + " 'hits': {'total': {'value': 2, 'relation': 'eq'},\n", + " 'max_score': 1.0,\n", + " 'hits': [{'_index': 'churn',\n", + " '_id': '0',\n", + " '_score': 1.0,\n", + " '_source': {'state': 'KS',\n", + " 'account length': 128,\n", + " 'area code': 415,\n", + " 'phone number': '382-4657',\n", + " 'international plan': 'no',\n", + " 'voice mail plan': 'yes',\n", + " 'number vmail messages': 25,\n", + " 'total day minutes': 265.1,\n", + " 'total day calls': 110,\n", + " 'total day charge': 45.07,\n", + " 'total eve minutes': 197.4,\n", + " 'total eve calls': 99,\n", + " 'total eve charge': 16.78,\n", + " 'total night minutes': 244.7,\n", + " 'total night calls': 91,\n", + " 'total night charge': 11.01,\n", + " 'total intl minutes': 10.0,\n", + " 'total intl calls': 3,\n", + " 'total intl charge': 2.7,\n", + " 'customer service calls': 1,\n", + " 'churn': 0}}]}}" + ] }, "execution_count": 6, "metadata": {}, @@ -114,7 +265,9 @@ "outputs": [ { "data": { - "text/plain": "{'acknowledged': True}" + "text/plain": [ + "{'acknowledged': True}" + ] }, "execution_count": 7, "metadata": {}, @@ -147,4 +300,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} diff --git a/tests/notebook/test_metrics.ipynb b/tests/notebook/test_metrics.ipynb index 08f76a4..0c7f0be 100644 --- a/tests/notebook/test_metrics.ipynb +++ b/tests/notebook/test_metrics.ipynb @@ -22,7 +22,7 @@ "metadata": {}, "outputs": [], "source": [ - "ed_df = ed.DataFrame('localhost', 'flights', columns=[\"AvgTicketPrice\", \"Cancelled\", \"dayOfWeek\", \"timestamp\", \"DestCountry\"])" + "ed_df = ed.DataFrame('http://localhost:9200', 'flights', columns=[\"AvgTicketPrice\", \"Cancelled\", \"dayOfWeek\", \"timestamp\", \"DestCountry\"])" ] }, { @@ -32,7 +32,13 @@ "outputs": [ { "data": { - "text/plain": "AvgTicketPrice 640.387285\nCancelled False\ndayOfWeek 3\ntimestamp 2018-01-21 23:43:19.256498944\ndtype: object" + "text/plain": [ + "AvgTicketPrice 640.387285\n", + "Cancelled False\n", + "dayOfWeek 3\n", + "timestamp 2018-01-21 23:43:19.256498944\n", + "dtype: object" + ] }, "execution_count": 3, "metadata": {}, @@ -51,7 +57,12 @@ "outputs": [ { "data": { - "text/plain": "AvgTicketPrice 640.387285\nCancelled 0.000000\ndayOfWeek 3.000000\ndtype: float64" + "text/plain": [ + "AvgTicketPrice 640.387285\n", + "Cancelled 0.000000\n", + "dayOfWeek 3.000000\n", + "dtype: float64" + ] }, "execution_count": 4, "metadata": {}, @@ -70,7 +81,14 @@ "outputs": [ { "data": { - "text/plain": "AvgTicketPrice 640.387285\nCancelled False\ndayOfWeek 3\ntimestamp 2018-01-21 23:43:19.256498944\nDestCountry NaN\ndtype: object" + "text/plain": [ + "AvgTicketPrice 640.387285\n", + "Cancelled False\n", + "dayOfWeek 3\n", + "timestamp 2018-01-21 23:43:19.256498944\n", + "DestCountry NaN\n", + "dtype: object" + ] }, "execution_count": 5, "metadata": {}, @@ -89,7 +107,11 @@ "outputs": [ { "data": { - "text/plain": "AvgTicketPrice 213.430365\ndayOfWeek 2.000000\ndtype: float64" + "text/plain": [ + "AvgTicketPrice 213.430365\n", + "dayOfWeek 2.000000\n", + "dtype: float64" + ] }, "execution_count": 6, "metadata": {}, @@ -108,7 +130,11 @@ "outputs": [ { "data": { - "text/plain": "AvgTicketPrice 213.430365\ndayOfWeek 2.000000\ndtype: float64" + "text/plain": [ + "AvgTicketPrice 213.430365\n", + "dayOfWeek 2.000000\n", + "dtype: float64" + ] }, "execution_count": 7, "metadata": {}, @@ -127,7 +153,14 @@ "outputs": [ { "data": { - "text/plain": "AvgTicketPrice 213.430365\nCancelled NaN\ndayOfWeek 2.0\ntimestamp NaT\nDestCountry NaN\ndtype: object" + "text/plain": [ + "AvgTicketPrice 213.430365\n", + "Cancelled NaN\n", + "dayOfWeek 2.0\n", + "timestamp NaT\n", + "DestCountry NaN\n", + "dtype: object" + ] }, "execution_count": 8, "metadata": {}, @@ -161,4 +194,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} diff --git a/tests/notebook/test_plotting.ipynb b/tests/notebook/test_plotting.ipynb index ddd89ec..89ba3fa 100644 --- a/tests/notebook/test_plotting.ipynb +++ b/tests/notebook/test_plotting.ipynb @@ -24,7 +24,7 @@ "metadata": {}, "outputs": [], "source": [ - "ed_df = ed.DataFrame('localhost', 'flights')" + "ed_df = ed.DataFrame('http://localhost:9200', 'flights')" ] }, { @@ -34,7 +34,9 @@ "outputs": [ { "data": { - "text/plain": "" + "text/plain": [ + "" + ] }, "execution_count": 3, "metadata": {}, @@ -42,8 +44,10 @@ }, { "data": { - "text/plain": "
", - "image/png": 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\n" + "image/png": 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", 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\n" 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", 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zxuoH5kbE3ZL2AlZJWpnGXRQR/5KfWNLBwMnAIcD+wLclvTaNvgw4BtgI3ClpeUTc35S1MGsc54h1tJoLMUkLgXcBWyPiDantXOAvgJ+nyT4RESvSuI8DZwLPAn8TETem9pnAxcA44IsRMb/WmFrNSE45nju9n9N9anLLiojNwOY0/KSkB4DJQ7xlFrAkIp4BHpLUBxyRxvVFxDoASUvStP6RsZbmHLFON5Y9YlcBnwOuHtTuLRizCiRNBQ4DbgfeApwt6TTgLrI9AtvJfoBuy71tI8//KG0Y1H5khWXMAeYAdHV10dvbWzGWrj2yIn+0qs1vrHbu3Flx3rXE2KhYilCmWKDx8TQjR9JyWjJPqhlLntSyjmX6To5E2fJosJoLsYi4NSXNSHgLxjqapD2Ba4GPRsQTki4HzgMiPV8AfHCsy4mIBcACgO7u7ujp6ak43aWLl3HB6tGn//pTK89vrHp7e6kUaxF7g6+aOb5iLEWo9rkUpZHxNCtHoHXzpJqx5Mnc6f2jXsdmr99YlS2PBmvEMWIN2YKBkW/F1FMjK+mRbIXUukVWL0UsP/95F70ls3PnzjHPQ9KuZD8wiyPiGwARsSU3/grg+vRyEzAl9/YDUhtDtJu1NOeIdbJ6F2IN24KBkW/F1FMjK+mRbMXUsrVST0UsP7+1VfSWzFiLQEkCrgQeiIgLc+2T0rExAO8B7kvDy4GvSLqQrBt/GnAHIGCapIPIflxOBt4/puDMSsA5Yp2urr+w3oIxe5G3AB8AVku6J7V9AjhF0qFkGy3rgQ8BRMQaSUvJuuf7gbMi4lkASWcDN5Kd2LIwItY0bzXMGsY5Yh2troWYt2DMXigivkf2PR9sxRDvOR84v0L7iqHeZ9aKnCPW6cZy+YqvAj3AvpI2AucAPd6CMTMzMxuZsZw1eUqF5iuHmN5bMGZmZmY5vrJ+HYzkwqxmZmZmg/lek2ZmZmYFcSFmZmZmVhAXYmZmZmYFcSFmZmZmVhAXYmZmZmYFcSFmZmZmVhAXYmZmZmYFcSFmZmZmVhAXYmZmZmYFcSFmZmZmVhAXYmZmZmYFcSFmZmZmVhAXYmYNJGmKpFsk3S9pjaSPpPZ9JK2UtDY9T0ztknSJpD5J90o6PDev2Wn6tZJmF7VOZvXkHLFOt0vRAZTJ1Hk3vKht7vR+Tq/QbjZC/cDciLhb0l7AKkkrgdOBmyNivqR5wDzgY8BxwLT0OBK4HDhS0j7AOUA3EGk+yyNie9PXyKy+nCPW0bxHzKyBImJzRNydhp8EHgAmA7OARWmyRcCJaXgWcHVkbgMmSJoEHAusjIht6YdlJTCzeWti1hjOEet03iNm1iSSpgKHAbcDXRGxOY16BOhKw5OBDbm3bUxt1doHL2MOMAegq6uL3t7eirF07ZHt7R2tavMbq507d1acdy0xNiqWIpQpFmh8PM3IkbSclsyTasaSJ7WsY5m+kyNRtjwazIWYWRNI2hO4FvhoRDwh6blxERGSoh7LiYgFwAKA7u7u6OnpqTjdpYuXccHq0af/+lMrz2+sent7qRRrEYcFXDVzfMVYilDtcylKI+NpVo6k+bVknlQzljyZO71/1OvY7PUbq7Ll0WDumjRrMEm7kv3ALI6Ib6TmLak7hfS8NbVvAqbk3n5AaqvWbtbynCPWyVyImTWQss36K4EHIuLC3KjlwMBZXbOBZbn209KZYUcBO1L3zI3ADEkT09ljM1KbWUtzjlinc9ekWWO9BfgAsFrSPantE8B8YKmkM4GHgZPSuBXA8UAf8DRwBkBEbJN0HnBnmu7TEbGtKWtg1ljOEetoLsTMGigivgeoyuijK0wfwFlV5rUQWFi/6MyK5xyxTueuSTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK8iYCjFJCyVtlXRfrm0fSSslrU3PE1O7JF0iqU/SvZIOz71ndpp+raTZlZZlZmZm1m7GukfsKmDmoLZ5wM0RMQ24Ob0GOA6Ylh5zgMshK9yAc4AjgSOAcwaKNzMzM7N2NqZCLCJuBQbfy2sWsCgNLwJOzLVfHZnbgAmSJgHHAisjYltEbAdW8uLizszMzKztNOJek10RsTkNPwJ0peHJwIbcdBtTW7X2F5E0h2xvGl1dXfT29tYvamDu9P4XtXXtUbm9WTpx+fm/686dO+v+dx6NnTt3FrZsMzNrfw296XdEhKSo4/wWAAsAuru7o6enp16zBuD0eTe8qG3u9H4uWF3cvdE7cfnrT+15bri3t5d6/51Ho8gi0MzM2l8jzprckrocSc9bU/smYEpuugNSW7V2MzMzs7bWiEJsOTBw5uNsYFmu/bR09uRRwI7UhXkjMEPSxHSQ/ozUZmZmZtbWxnr5iq8C3wdeJ2mjpDOB+cAxktYC70yvAVYA64A+4ArgrwAiYhtwHnBnenw6tZm1vCqXeDlX0iZJ96TH8blxH0+XeHlQ0rG59pmprU/SvMHLMWtVzhHrdGM6+CciTqky6ugK0wZwVpX5LAQWjiUWs5K6CvgccPWg9osi4l/yDZIOBk4GDgH2B74t6bVp9GXAMWQns9wpaXlE3N/IwM2a5CqcI9bBijsK3KwDRMStkqaOcPJZwJKIeAZ4SFIf2bX1APoiYh2ApCVpWv/IWMtzjlincyFmVoyzJZ0G3AXMTdfQmwzclpsmfymXwZd4ObLSTEd6iZdaL0vSqLNIq12mpIhLtxR9yZS8MsUCTY+nITkCrZsn1YwlT2pZxzJ9J0eibHk0mAsxs+a7nOy4yEjPFwAfrMeMR3qJl0sXL6vpsiT5S4vUU7XLlFS6pEyjXTVzfKGXTMkr+vItgzUxnoblCLRunlQzljyp5RJFzV6/sSpbHg3mQsysySJiy8CwpCuA69PLoS7l4ku8WMdwjlgnacTlK8xsCAPX2UveAwycLbYcOFnS7pIOIrsv6x1kZxNPk3SQpN3IDlZe3syYzZrJOWKdxHvEzBooXeKlB9hX0kayG9z3SDqUrNtlPfAhgIhYI2kp2QHG/cBZEfFsms/ZZNfXGwcsjIg1zV0Ts8ZwjlincyFm1kBVLvFy5RDTnw+cX6F9Bdm1+MzainPEOp27Js3MzMwK0pZ7xKYWcKaVmZmZ2Wh5j5iZmZlZQdpyj5iZWa1Wb9pR03WZ1s8/oQHRmJVPrb1OzpHKvEfMzMzMrCAuxMzMzMwK4kLMzMzMrCAuxMzMzMwK4kLMzMzMrCAuxMzMzMwK4stXWOnkT42eO71/xJcS8KnRZmbWarxHzMzMzKwgLsTMzMzMCuJCzMzMzKwgLsTMzMzMCuJCzMzMzKwgLsTMGkjSQklbJd2Xa9tH0kpJa9PzxNQuSZdI6pN0r6TDc++ZnaZfK2l2Eeti1ijOE+tkLsTMGusqYOagtnnAzRExDbg5vQY4DpiWHnOAyyH7QQLOAY4EjgDOGfhRMmsTV+E8sQ7lQsysgSLiVmDboOZZwKI0vAg4Mdd+dWRuAyZImgQcC6yMiG0RsR1YyYt/tMxalvPEOpkv6GrWfF0RsTkNPwJ0peHJwIbcdBtTW7X2F5E0h2wvAV1dXfT29lYOYI/sYrmjVW1+Y7Vz586K864lxrEq02dT7XMpSpPjcZ6M0FjypNZ1rEVR3+Wy5dFgLsTMChQRISnqOL8FwAKA7u7u6OnpqTjdpYuXccHq0af/+lMrz2+sent7qRTrSO+qUE9zp/eX5rOp9rkUpah4nCdDG0ue1Pp9r0WzP5cBZcujwdw1adZ8W1JXCul5a2rfBEzJTXdAaqvWbtbOnCfWEVyImTXfcmDgjK7ZwLJc+2nprLCjgB2pa+ZGYIakieng4xmpzaydOU+sI7hr0qyBJH0V6AH2lbSR7Kyu+cBSSWcCDwMnpclXAMcDfcDTwBkAEbFN0nnAnWm6T0fE4AObzVqW88Q6WcMKMUnrgSeBZ4H+iOhOpxd/DZgKrAdOiojtkgRcTJZcTwOnR8TdjYrNrFki4pQqo46uMG0AZ1WZz0JgYR1DMysN54l1skZ3Tb49Ig6NiO70elTXhTEzMzNrZ80+Rmy014UxMzMza1uNPEYsgJvSKcdfSKcLj/a6MJtzbSO+9ks9r4nSzGusePljW36jruNkZmbWKI0sxN4aEZskvRJYKelH+ZG1XBdmpNd+qee1h5p5jRUvf2zLb9R1nMzMzBqlYV2TEbEpPW8FriO799dorwtjZmZm1rYasqtD0njgJRHxZBqeAXya568LM58XXxfmbElLyG7YuiPXhWlmJTG1xr3N6+efUOdIzMrLeWKj0ag+py7guuyqFOwCfCUiviXpTkZxXRgzMzOzdtaQQiwi1gFvrND+GKO8LoyZtb7h9hDMnd5fyH0lzcqk1j1p1tp8ZX1rG2P5J+YuATMzK4LvNWlmZmZWEBdiZmZmZgVx16SZWR34TDmzoTlHKvMeMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzK4ik9ZJWS7pH0l2pbR9JKyWtTc8TU7skXSKpT9K9kg4vNnqz5nCeWLvzwfpmxXp7RDyaez0PuDki5kual15/DDgOmJYeRwKXp2drcUMdwDzUhW7b/QDmQZwnHazdD/L3HjGzcpkFLErDi4ATc+1XR+Y2YIKkSQXEZ1YGzhNrG94jZlacAG6SFMAXImIB0JW74f0jZPdtBZgMbMi9d2Nq25xrQ9IcYA5AV1cXvb29FRfctUe2t6UsyhRPq8RS7W/bSDt37ixiuc6TBmrndRz4uxb0vR0xF2JmxXlrRGyS9EpgpaQf5UdGRKQfnxFLP1ILALq7u6Onp6fidJcuXsYFq8uT/nOn95cmnlaJZf2pPc0NhuyHrdp3qoGcJw1Upu97vQ3kSEHf2xFz16RZQSJiU3reClwHHAFsGehKSc9b0+SbgCm5tx+Q2szamvPE2l17lsFmJSdpPPCSiHgyDc8APg0sB2YD89PzsvSW5cDZkpaQHXy8I9c1Yx2o3Q9gBueJjc1Ajgx10kslzc4RF2JmxegCrpMEWR5+JSK+JelOYKmkM4GHgZPS9CuA44E+4GngjOaHbNZ0zhNrey7EzAoQEeuAN1Zofww4ukJ7AGc1ITSz0nCeWCfwMWJmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBdml6ADMzKx5ps67oab3rZ9/Qp0jMSunWnMEassT7xEzMzMzK0hpCjFJMyU9KKlP0ryi4zErG+eI2fCcJ9ZqSlGISRoHXAYcBxwMnCLp4GKjMisP54jZ8Jwn1opKUYgBRwB9EbEuIn4FLAFmFRyTWZk4R8yG5zyxlqOIKDoGJL0XmBkRf55efwA4MiLOHjTdHGBOevk64MEmhLcv8GgTluPll3f54yNivwJjaESOFP25DlameBxLddXiObDoHIGOyJNG8Do2T8U8aamzJiNiAbCgmcuUdFdEdDdzmV5+6ZY/tajlj9ZIc6Toz3WwMsXjWKorWzy1atU8aQSvY/HK0jW5CZiSe31AajOzjHPEbHjOE2s5ZSnE7gSmSTpI0m7AycDygmMyKxPniNnwnCfWckrRNRkR/ZLOBm4ExgELI2JNwWENaGpXqJfv5VfSgBwpxXrllCkex1Jd2eJ5gQ7Ik0bwOhasFAfrm5mZmXWisnRNmpmZmXUcF2JmZmZmBenoQkzSFEm3SLpf0hpJH0nt50raJOme9Dg+956Pp1tnPCjp2DrEsF7S6rScu1LbPpJWSlqbniemdkm6JC3/XkmHj3HZr8ut4z2SnpD00Uavv6SFkrZKui/XNup1ljQ7Tb9W0uwxLPv/SfpRmv91kiak9qmSfpH7HD6fe8+b0t+tL8WnWj6LIqhJt4Cp13e7jn/nui17tH//KvGMOs+q/e2UHZx+e2r/mrID1avFUu3/XmGfT9k0K0capcjca5Sy5XRdRUTHPoBJwOFpeC/gx2S3xTgX+LsK0x8M/BDYHTgI+AkwbowxrAf2HdT2z8C8NDwP+GwaPh74JiDgKOD2On4W44BHgAMbvf7A24DDgftqXWdgH2Bdep6YhifWuOwZwC5p+LO5ZU/NTzdoPnekeJTiO67o7/Mo/s4/AV4N7Jb+ngc3aFlj/m7X+e9ct2WP9u9fJZ5R5dlQfztgKXByGv488JdDxFLt/15hn0+ZHs3MkQauQ2G518B1KlVO1/PR0XvEImJzRNydhp8EHgAmD/GWWcCSiHgmIh4C+shuqVFvs4BFaXgRcGKu/erI3AZMkDSpTss8GvhJRDw8TFxjXv+IuBXYVmHeo1nnY4GVEbEtIrYDK4GZtSw7Im6KiP708jayaw9VlZb/8oi4LbIMvjoXb9kVfQuYwv7O9Vp2LX//KvFUUy3PKv7t0pb7O4CvV1i3SrFU+79X2OdTMkXnSKM0JfcapWw5XU8dXYjlSZoKHAbcnprOTrs0Fw7s7iT7Z7Uh97aNDF24jUQAN0lapey2GwBdEbE5DT8CdDVw+QNOBr6ae92s9R8w2nVuVCwfJNsaGnCQpB9I+i9Jf5CLaWMDlt0MjfwbDlaP73Y9463Xsuv59x9NnlVrfwXweG5jYsTxDPq/V8bPpwjNzJFGKVvuNUpbfGddiAGS9gSuBT4aEU8AlwO/DRwKbAYuaODi3xoRhwPHAWdJelt+ZKrOG3qNkXQ8yR8B/56amrn+L9KMda5E0ieBfmBxatoMvCoiDgP+FviKpJc3O64WVvh3u5oil51TaJ5V+L/3nJJ8Pla70uZeo7TyOnV8ISZpV7J/Rosj4hsAEbElIp6NiN8AV/B891vdb58REZvS81bgurSsLQNdjul5a6OWnxwH3B0RW1IsTVv/nNGuc11jkXQ68C7g1JTQpK6hx9LwKrLjRl6blpPvvmyl26g07RYwdfpu1zPeei27Ln//GvKsWvtjZF0vuwxqr6rS/z1K9vkUqOVvk1TC3GuUtvjOdnQhlo6tuBJ4ICIuzLXnj7t6DzBwlsZy4GRJu0s6CJhGdoBfrcsfL2mvgWGyg8bvS8sZOJtjNrAst/zT0hkhRwE7crtlx+IUct2SzVr/QUa7zjcCMyRNTF06M1LbqEmaCfwD8EcR8XSufT9J49Lwq8nWd11a/hOSjkrfodNy8ZZdU24BU8fvdt3+zvVadr3+/jXkWcW/XdpwuAV4b4V1q7Tciv/3KNnnU6CWvk1SSXOvUdrjOxslOMOjqAfwVrJdmfcC96TH8cA1wOrUvhyYlHvPJ8n2jDzIGM+qIDsr54fpsQb4ZGp/BXAzsBb4NrBPahdwWVr+aqC7Dp/BeLIt6r1zbQ1df7KibzPwa7K++DNrWWey47n60uOMMSy7j+y4gYHvwOfTtH+S/i73AHcD787Np5vsn9tPgM+R7lLRCo/0Hf9xiv2TDVpG3b7bdfw7123Zo/37V4ln1HlW7W+XPu87Upz/Duw+RCzV/u8V9vmU7dGMHGlg7IXmXgPXq1Q5Xc+Hb3FkZmZmVpCO7po0MzMzK5ILMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLsQaQNFVSSNolvf6mpNkjfG+vpD9vbITFLlPSJyR9sVnLs3IpS35IOlXSTfWYl1kjOWdeEMPnJf3vImOoNxdiYyRpvaRfSNo58AD2z08TEcdFxKI6LOsFyZjaTpf0bG75D0n6kqTXjnV5NcTXm+J746D261J7D0BE/FNENLXYtGIUmR+p4B9Y7i8H5cmaiFgcETPGutwqsayX9M5BbadL+l4jlmfto8Nz5leS9h3U/oMU41SAiPhwRJzXiBiK4kKsPt4dEXsOPICfNXn530/L3Rt4J/ALYJWkNzQ5DoAfA6cNvJD0CuDNwM8LiMXKoZD8SAX/wDI/TMqT9DikGTE0Q37DzNpGp+bMQ8ApAy8kTQde1oTlFsqFWBPkdw1LGifpAkmPpr1XZw/eywUcKOm/JT0p6abcFsKt6fnxtIXy5vxyIuLZiPhJRPwV8F/AubkYjpL0P5Iel/TDgb1TFWL9bUnfkfRYinGxpAlp3N9LunbQ9JdIujjXtBj4U0nj0utTgOuAX+Xec66kL6fhgS2y2ZJ+mpb5yeE+U2sfzcqPCst9wR6qtJy/krQ2zfu8lA//I+kJSUsl7Zab/l2S7kk59T+SfneU6/36tO6PS1oj6Y8qfSZDxHqWpLXA2tEs11pfG+fMNeQ25IHZwNWDYrhK0mfScI+kjZLmStoqabOkM0bwEZaKC7Hm+wvgOOBQ4HDgxArTvB84A3glsBvwd6n9bel5QtpC+f4Qy/kG8AcAkiYDNwCfAfZJ87tW0n4V3ifg/5LtCn89MIXnC7ovAzNzhdkuwMm8MFF+BtwPDOy+Pm3Q+GreCrwOOBr4P5JeP4L3WPtpVn5UcyzwJuAo4B+ABcCfkeXBG0hb65IOAxYCHwJeAXwBWC5p95EsRNKuwH8CN6X1+GtgsaTXjSLWE4EjgYNH8R5rP+2UM7cBL08bKePIfl++PMzyf4usN2gycCZwmaSJNaxHYVyI1cd/pAr/cUn/Mcy0JwEXR8TGiNgOzK8wzZci4scR8QtgKVmCjdbPyIouyJJiRUSsiIjfRMRK4C7g+MFvioi+iFgZEc9ExM+BC4E/TOM2k21BvS9NPhN4NCJWDZrN1cBpkn6HLMFHktyfiohfRMQPgR8CbxzuDdYyypgf1fxzRDwREWuA+4CbImJdROwAvgkclqabA3whIm5Pe6IXAc+Q/RgNyK/348C/5cYdBewJzI+IX0XEd4DryXXLjMD/jYht6XOw9tKpOQPP7xU7BngA2DTM8n8NfDoifh0RK4CdZBv1LcOFWH2cGBET0uPEYabdH9iQe72hwjSP5IafJvuHPVqTgW1p+EDgfYN+FN4KTBr8JkldkpZI2iTpCbKtkfzBk4vICjvS8zUVlv0N4B3A2VXGV1KPdbZyKmN+VLMlN/yLCq8HlnUgMHdQTk3hhQdV59d7AvBXuXH7Axsi4je5tofJ8nakKn021h46NWcg+814P3A6I+tNeSwi+nOvW+73w4VY820GDsi9njKK98Yopn0P8N00vAG4Jv+jEBHjI6LSltM/peVMj4iXkxVbyo3/D+B3lZ0I8C6yY8JeGGTE02RbQn/JyAsxM2hefozVBuD8QTn1soj46gjf/zNgiqT8/+BX8fzW/1O88CDl36owj2aur5VXW+VMRDxMdtD+8WQb9W3PhVjzLQU+ImlyOtbqY6N478+B3wCvrjQyHbR5kKRLgR7gU2nUl4F3Szo2TfPSdJDjARVmsxfZrt0d6diyv8+PjIhfAl8HvgLcERE/rRLrJ4A/jIj1o1g/s4blR51dAXxY0pHKjJd0gqS9Rvj+28m23P9B0q7KTp55N7Akjb8H+GNJL5P0GrJjX8wqacecORN4R0Q81YS4CudCrPmuIDtA917gB8AKoB94drg3pj1N5wP/nXbtDvStv1nZtWaeAHqBlwO/FxGr0/s2ALPIiqOfk22Z/D2V//6fIjvgcwfZAf6VtkgWAdMZYm9XRPwsInzNJButRuRH3UXEXWQHSX8O2A70kXWljPT9vyIrvI4DHiU7fuy0iPhRmuQisjONt5Dl24v2PJslbZcz6ez/uxoVS9kownu3iyTpOODzEXFg0bGMlKRXAT8Cfisinig6HmtfrZgfZkVyzrQe7xFrMkl7SDpe0i6p6+8csutstYR0TMvfAktchFm9tXp+mDWbc6b1eY9Yk0l6GdnFVn+H7IySG4CPtEJRI2k8WVfJw8DM1OVpVjetnB9mRXDOtD4XYmZmZmYFcdekmZmZWUFciJmZmZkVZJfhJymnfffdN6ZOnVpx3FNPPcX48eObG9AIObbaFBnbqlWrHo2ISvflLLVWzZFqWi3mToq3VXMEWi9PHNPIlDGmqnkSES35eNOb3hTV3HLLLVXHFc2x1abI2IC7ogTf+dE+WjVHqmm1mDsp3lbNkWjBPHFMI1PGmKrlibsmzczMzAriQszMzMysIC7EzMzMzAoybCEmaaGkrZLuy7XtI2mlpLXpeWJql6RLJPVJulfS4bn3zE7Tr5U0O9f+Jkmr03sukaR6r6SZmZlZGY1kj9hVwMxBbfOAmyNiGnBzeg3ZDWynpccc4HLICjey2y4cCRwBnDNQvKVp/iL3vsHLMjMzM2tLw16+IiJulTR1UPMsoCcNLwJ6gY+l9qvT2QG3SZogaVKadmVEbAOQtBKYKakXeHlE3JbarwZOBL45lpVavWkHp8+7YdTvWz//hLEs1syqmFpDPoJz0jrLSPJk7vT+F/2+OU9aW63XEeuKiM1p+BGgKw1PBvL3H9yY2oZq31ihvSJJc8j2tNHV1UVvb2/l4PbIvqyjVW1+9bRz586mLKcWjs3MzKy5xnxB14gISU25YWVELAAWAHR3d0dPT0/F6S5dvIwLVo9+1dafWnl+9dTb20u1uIvm2MzMzJqr1rMmt6QuR9Lz1tS+CZiSm+6A1DZU+wEV2s3MzMzaXq2F2HJg4MzH2cCyXPtp6ezJo4AdqQvzRmCGpInpIP0ZwI1p3BOSjkpnS56Wm5eZmZlZWxu2/07SV8kOtt9X0kaysx/nA0slnQk8DJyUJl8BHA/0AU8DZwBExDZJ5wF3puk+PXDgPvBXZGdm7kF2kP6YDtQ3MzMzaxUjOWvylCqjjq4wbQBnVZnPQmBhhfa7gDcMF4eZmZlZu/GV9c0aSNJLJd0h6YeS1kj6VGo/SNLt6ULGX5O0W2rfPb3uS+On5ub18dT+oKRjC1ols7pyjlincyFm1ljPAO+IiDcCh5JdP+8o4LPARRHxGmA7cGaa/kxge2q/KE2HpIOBk4FDyC56/G+SxjVzRcwaxDliHc2FmFkDRWZnerlregTwDuDrqX0R2YWMIbso8qI0/HXg6HQiyyxgSUQ8ExEPkR2HeUTj18CssZwj1unGfB0xMxta2ipfBbwGuAz4CfB4RAxcdTh/IePnLn4cEf2SdgCvSO235WZb8eLHI73ocbMvkFvLBZbhhRdZbrWL+jrekWtmjqTltWyeVLpgedHfszJ+18sYUzUuxMwaLCKeBQ6VNAG4DvidBi5rRBc9bvYFcmu55Ri88CLLrXZRX8c7cs3MkbS8ls2TudP7X3TB8mZcjHwoZfyulzGmatw1adYkEfE4cAvwZmCCpIH/pvkLGT938eM0fm/gMapfFNmsbThHrBO5EDNrIEn7pa18JO0BHAM8QPZj89402eCLIg9cLPm9wHfSZWGWAyenM8YOAqYBdzRlJcwayDlinc5dk2aNNQlYlI6BeQmwNCKul3Q/sETSZ4AfAFem6a8ErpHUB2wjOwuMiFgjaSlwP9APnJW6c8xanXPEOpoLMbMGioh7gcMqtK+jwhldEfFL4H1V5nU+cH69YzQrknPEOp0LMbMOtHrTjpoOoF8//4QGRGNWTs4TawYfI2ZmZmZWEBdiZmZmZgVxIWZmZmZWEBdiZmZmZgVxIWZmZmZWEBdiZmZmZgVxIWZmZmZWEBdiZmZmZgVxIWZmZmZWkDEVYpL+l6Q1ku6T9FVJL5V0kKTbJfVJ+pqk3dK0u6fXfWn81Nx8Pp7aH5R07BjXyczMzKwl1FyISZoM/A3QHRFvAMaR3Xz1s8BFEfEaYDtwZnrLmcD21H5Rmg5JB6f3HQLMBP4t3fzVzMzMrK2NtWtyF2APSbsALwM2A+8Avp7GLwJOTMOz0mvS+KMlKbUviYhnIuIhoI8KN3o1MzMzazc13/Q7IjZJ+hfgp8AvgJuAVcDjEdGfJtsITE7Dk4EN6b39knYAr0jtt+VmnX/PC0iaA8wB6Orqore3t2JsXXvA3On9FccNpdr86mnnzp1NWU4tHFv9SZoCXA10AQEsiIiLJZ0L/AXw8zTpJyJiRXrPx8n2ID8L/E1E3JjaZwIXk+19/mJEzG/muhRhau6Gy3On94/4Bsy+6XLrcI6M3dQabkwOzpOyqLkQkzSRbG/WQcDjwL+TdS02TEQsABYAdHd3R09PT8XpLl28jAtWj37V1p9aeX711NvbS7W4i+bYGqIfmBsRd0vaC1glaWUad1FE/Et+4kFd9fsD35b02jT6MuAYso2VOyUtj4j7m7IWZo3jHLGOVnMhBrwTeCgifg4g6RvAW4AJknZJe8UOADal6TcBU4CNqStzb+CxXPuA/HvMWlpEbCbrsicinpT0AFX2+CbPddUDD0nKd9X3RcQ6AElL0rT+kbGW5hyxTjeWQuynwFGSXkbWNXk0cBdwC/BeYAkwG1iWpl+eXn8/jf9ORISk5cBXJF1ItnUzDbhjDHGZlVI6U/gw4HayjZazJZ1GljdzI2I7Q3fVbxjUfmSFZZSy+76WZQ02mpjL0I3dat3pZYi3GTmSltOyeVJrTJXU6+9dhu/OYGWMqZqxHCN2u6SvA3eT7Vr+AVm34Q3AEkmfSW1XprdcCVyTtl62ke1aJiLWSFpKttXSD5wVEc/WGpdZGUnaE7gW+GhEPCHpcuA8smNizgMuAD441uWUtft+pMd2DWXu9P4Rx9yMwwyG02rd6UXH26wcgdbOk9HkwXDqlSdFf3cqKWNM1YzprxkR5wDnDGpeR4WzHiPil8D7qsznfOD8scRiVlaSdiX7gVkcEd8AiIgtufFXANenl0N11bsL39qSc8Q6ma+sb9ZA6RItVwIPRMSFufZJucneA9yXhpcDJ6cLIB/E8131dwLT0gWTdyPbo7y8Getg1kjOEet09dm/aWbVvAX4ALBa0j2p7RPAKZIOJet2WQ98CIbuqpd0NnAj2an5CyNiTfNWw6xhnCPW0VyImTVQRHwPUIVRK4Z4T8Wu+nQNparvM2tFzhHrdO6aNDMzMyuICzEzMzOzgrgQMzMzMyuICzEzMzOzgrgQMzMzMyuICzEzMzOzgrgQMzMzMyuICzEzMzOzgrgQMzMzMyuICzEzMzOzgrgQMzMzMyuICzEzMzOzgrgQMzMzMyuICzGzBpI0RdItku6XtEbSR1L7PpJWSlqbniemdkm6RFKfpHslHZ6b1+w0/VpJs4taJ7N6co5Yp3MhZtZY/cDciDgYOAo4S9LBwDzg5oiYBtycXgMcB0xLjznA5ZD9KAHnAEcCRwDnDPwwmbU454h1NBdiZg0UEZsj4u40/CTwADAZmAUsSpMtAk5Mw7OAqyNzGzBB0iTgWGBlRGyLiO3ASmBm89bErDGcI9bpdik6ALNOIWkqcBhwO9AVEZvTqEeArjQ8GdiQe9vG1FatffAy5pDtJaCrq4ve3t6KsXTtAXOn9496HarNbzi1LGuw0cRca5z1tHPnzlLEMVJliLcZOZKW07J5UmtMldTr712G785gZYypmjEVYpImAF8E3gAE8EHgQeBrwFRgPXBSRGyXJOBi4HjgaeD0ga2g1Jf/j2m2n4mIRZi1EUl7AtcCH42IJ7J0yERESIp6LCciFgALALq7u6Onp6fidJcuXsYFq0ef/utPrTy/4Zw+74aa3pc3d3r/iGOuNc566u3tpdrnX0ZFx9usHEnza9k8GU0eDKdeeVL0d6eSMsZUzVi7Ji8GvhURvwO8kWyXsvv1zXIk7Ur2A7M4Ir6Rmrek7hTS89bUvgmYknv7AamtWrtZy3OOWCeruRCTtDfwNuBKgIj4VUQ8jvv1zZ6T9gRfCTwQERfmRi0HBs7qmg0sy7Wfls4MOwrYkbpnbgRmSJqYNlRmpDazluYcsU43lv2bBwE/B74k6Y3AKuAjdGC//miUud/asTXEW4APAKsl3ZPaPgHMB5ZKOhN4GDgpjVtB1n3fR9aFfwZARGyTdB5wZ5ru0xGxrSlrYNZYzhHraGMpxHYBDgf+OiJul3Qxz3dDAp3Trz8aZe63dmz1FxHfA1Rl9NEVpg/grCrzWggsrF90ZsVzjlinG8sxYhuBjRFxe3r9dbLCzP36ZmZmZiNQcyEWEY8AGyS9LjUdDdyP+/XNzMzMRmSs58D+NbBY0m7AOrK++pfgfn0zMzOzYY2pEIuIe4DuCqPcr29mZmY2DN/iyMzMzKwgLsTMzMzMCuJCzMzMzKwgLsTMzMzMCuJCzMzMzKwgLsTMzMzMCuJCzMzMzKwgY72gq5l1kKnzbig6BLPSc57YaHiPmJmZmVlBXIiZmZmZFcSFmFkDSVooaauk+3Jt50raJOme9Dg+N+7jkvokPSjp2Fz7zNTWJ2les9fDrFGcI9bpfIyYWWNdBXwOuHpQ+0UR8S/5BkkHAycDhwD7A9+W9No0+jLgGGAjcKek5RFxfyMDb2VjOUZn/fwT6hiJjcBVOEcKUWueOEfqy4WYWQNFxK2Spo5w8lnAkoh4BnhIUh9wRBrXFxHrACQtSdP6R8ZannPEOp0LMbNinC3pNOAuYG5EbAcmA7flptmY2gA2DGo/stJMJc0B5gB0dXXR29tbceFde8Dc6f1jib/pmhVztc9stHbu3Fm3eTVDCeNtSI5Aa+dJGWIa/HmV8LtTypiqcSFm1nyXA+cBkZ4vAD5YjxlHxAJgAUB3d3f09PRUnO7Sxcu4YHVrpf/c6f1NiXn9qT11mU9vby/VPv8yKlm8DcsRaO08aVYeDGVwjpTsuwOUM6ZqyvUNM+sAEbFlYFjSFcD16eUmYEpu0gNSG0O0m7Ud54h1Ep81adZkkiblXr4HGDhbbDlwsqTdJR0ETAPuAO4Epkk6SNJuZAcrL29mzGbN5ByxTuI9YmYNJOmrQA+wr6SNwDlAj6RDybpd1gMfAoiINZKWkh1g3A+cFRHPpvmcDdwIjAMWRsSa5q6JWWM4R6zTuRAza6CIOKVC85VDTH8+cH6F9hXAijqGZlYKzhHrdGPumpQ0TtIPJF2fXh8k6fZ0Ub2vpd3EpF3JX0vtt+dPV652gT4zMzOzdlaPY8Q+AjyQe/1ZsgvxvQbYDpyZ2s8Etqf2i9J0gy/QNxP4N0nj6hCXmZmZWamNqRCTdABwAvDF9FrAO4Cvp0kWASem4VnpNWn80Wn65y7QFxEPAfkL9JmZmZm1rbEeI/avwD8Ae6XXrwAej4iBq83lL7Y3mXTBvYjol7QjTT/UBfpeoNEX4WvGxd/KfJE5x2ZmZtZcNRdikt4FbI2IVZJ66hbREBp9Eb56XchxKGW+yJxjMzMza66x7BF7C/BHko4HXgq8HLgYmCBpl7RXLH9RvYEL8W2UtAuwN/AYQ1+gz8zMzKxt1XyMWER8PCIOiIipZAfbfyciTgVuAd6bJpsNLEvDy9Nr0vjvRERQ/QJ9ZmZmZm2tEdcR+xiwRNJngB/w/PVgrgSukdQHbCMr3oa8QJ+ZmZlZO6tLIRYRvUBvGl5HhbMeI+KXwPuqvL/iBfrMzMzM2pnvNWlmZmZWEBdiZmZmZgVxIWZmZmZWEBdiZmZmZgVxIWbWQJIWStoq6b5c2z6SVkpam54npnZJukRSn6R7JR2ee8/sNP1aSbMrLcusVTlPrJO5EDNrrKvIbmafNw+4OSKmATen1wDHkV1HbxrZrbwuh+wHCTgHOJLsjORzBn6UzNrEVThPrEO5EDNroIi4ley6eXmzgEVpeBFwYq796sjcRnaXiknAscDKiNgWEduBlbz4R8usZTlPrJM14oKuZja0rojYnIYfAbrS8GRgQ266jamtWvuLSJpDtpeArq6uqjdK79oD5k7vrzH8YjQr5nrdXL7VblRfwnidJxWUIabBn1cJvzuljKkaF2JmBYqIkBR1nN8CYAFAd3d3VLtR+qWLl3HB6tZK/7nT+5sS8/pTe+oyn1a7UX2Z43WePK9ZeTCUwTlSxu9OGWOqxl2TZs23JXWlkJ63pvZNwJTcdAektmrtZu3MeWIdwYWYWfMtBwbO6JoNLMu1n5bOCjsK2JG6Zm4EZkiamA4+npHazNqZ88Q6Qrn2uZq1GUlfBXqAfSVtJDuraz6wVNKZwMPASWnyFcDxQB/wNHAGQERsk3QecGea7tMRMfjAZrOW5TyxTuZCzKyBIuKUKqOOrjBtAGdVmc9CYGEdQzMrDeeJdTJ3TZqZmZkVxIWYmZmZWUFciJmZmZkVxIWYmZmZWUFciJmZmZkVxIWYmZmZWUF8+Qozs5yp826o6X3r559Q50jMymlwjsyd3s/pI8gb50hlNe8RkzRF0i2S7pe0RtJHUvs+klZKWpueJ6Z2SbpEUp+keyUdnpvX7DT9Wkmzqy3TzMzMrJ2MpWuyH5gbEQcDRwFnSToYmAfcHBHTgJvTa4DjgGnpMQe4HLLCjewqykcCRwDnDBRvZmZmZu2s5kIsIjZHxN1p+EngAWAyMAtYlCZbBJyYhmcBV0fmNmBCupHrscDKiNgWEduBlcDMWuMyMzMzaxV1OUZM0lTgMOB2oCvdgBXgEaArDU8GNuTetjG1VWuvtJw5ZHvT6Orqore3t2I8XXtkfdajVW1+9bRz586mLKcWjs3MzKy5xlyISdoTuBb4aEQ8Iem5cRERkmKsy8jNbwGwAKC7uzt6enoqTnfp4mVcsHr0q7b+1Mrzq6fe3l6qxV00x2ZmZtZcY7p8haRdyYqwxRHxjdS8JXU5kp63pvZNwJTc2w9IbdXazczMzNraWM6aFHAl8EBEXJgbtRwYOPNxNrAs135aOnvyKGBH6sK8EZghaWI6SH9GajNra5LWS1ot6R5Jd6W2UZ91bNbOnCfW7sayR+wtwAeAd6QEuUfS8cB84BhJa4F3ptcAK4B1QB9wBfBXABGxDTgPuDM9Pp3azDrB2yPi0IjoTq9HddaxWYdwnljbqvkYsYj4HqAqo4+uMH0AZ1WZ10JgYa2xmLWRWUBPGl4E9AIfI3fWMXCbpAmSJuVOjDHrJM4Taxu+sr5ZcQK4KZ3Q8oV0Mspozzp+wQ9Mo88sLlLZYx78Wbfamb4ljtd5ktPKMTXz+1Xi7/OLuBAzK85bI2KTpFcCKyX9KD+ylrOOG31mcZHmTu8vdcyDz7putTN9Sxyv8ySnjHkw0piacWWCASX+Pr+Ib/ptVpCI2JSetwLXkd1ZYrRnHZu1NeeJtTsXYmYFkDRe0l4Dw2RnC9/H6M86NmtbzhPrBOXav2nWObqA69IFkHcBvhIR35J0J7BU0pnAw8BJafoVwPFkZx0/DZzR/JDNms55Ym3PhZhZASJiHfDGCu2PMcqzjs3alfPEOoG7Js3MzMwK4kLMzMzMrCAuxMzMzMwK4mPEzMzqYOq8G17weu70fk4f1FbJ+vknNCoks1IZnCMj1e454j1iZmZmZgVxIWZmZmZWEBdiZmZmZgVxIWZmZmZWEBdiZmZmZgXxWZNmZgXymWRmQ6slR+ZO76en/qE0hAsxaxj/wJiZmQ3NhVgdjKbgyF9bqNkFx3BxVrvuUasURrUWftA662hmZu3FhZgNaywFjpmZmVVXmkJM0kzgYmAc8MWImN/sGJpdcLjAqazS5zLSq5S3szLkiFnZOU+s1ZSiEJM0DrgMOAbYCNwpaXlE3F9sZGbl4ByxwXwM5os5TyyvVXKkLJevOALoi4h1EfErYAkwq+CYzMrEOWI2POeJtZxS7BEDJgMbcq83AkcWFItZGTlHrC6q7SUYrvu/RfakOU9szJp94ldZCrERkTQHmJNe7pT0YJVJ9wUebU5Uo/M3jq0mjY5Nnx1y9IGNWm69tUOOVFPm72cl7RZvu+QItHaelPF75ZieV0uelKUQ2wRMyb0+ILW9QEQsABYMNzNJd0VEd/3Cqx/HVpsyx9YkHZMj1bRazI63EG2fJ45pZMoYUzVlOUbsTmCapIMk7QacDCwvOCazMnGOmA3PeWItpxR7xCKiX9LZwI1kpxwvjIg1BYdlVhrOEbPhOU+sFZWiEAOIiBXAijrNbthdzgVybLUpc2xN0UE5Uk2rxex4C9ABeeKYRqaMMVWkiCg6BjMzM7OOVJZjxMzMzMw6TlsVYpJmSnpQUp+keQUsf6GkrZLuy7XtI2mlpLXpeWJql6RLUqz3Sjq8wbFNkXSLpPslrZH0kbLEJ+mlku6Q9MMU26dS+0GSbk8xfC0dfIuk3dPrvjR+aqNia0dF58lwRpNHZTDa3CqD0eZcpykyRyStl7Ra0j2S7kptTf0/Xa/fMkmz0/RrJc1uQEznStqUPqt7JB2fG/fxFNODko7NtZfv/19EtMWD7MDMnwCvBnYDfggc3OQY3gYcDtyXa/tnYF4angd8Ng0fD3wTEHAUcHuDY5sEHJ6G9wJ+DBxchvjSMvZMw7sCt6dlLgVOTu2fB/4yDf8V8Pk0fDLwtaK/f63yKEOejCDGEedRGR6jza0yPEabc530KDpHgPXAvoPamvp/uh6/ZcA+wLr0PDENT6xzTOcCf1dh2oPT32134KD09xxX9N+22qOd9ogVfmuLiLgV2DaoeRawKA0vAk7MtV8dmduACZImNTC2zRFxdxp+EniA7CrUhceXlrEzvdw1PQJ4B/D1KrENxPx14GhJakRsbajwPBnOKPOocDXkVuFqyLlOUsYcaer/6Tr9lh0LrIyIbRGxHVgJzKxzTNXMApZExDMR8RDQR/Z3LePftq0KsUq3tphcUCx5XRGxOQ0/AnSl4cLiTV15h5FtBZciPknjJN0DbCVL2J8Aj0dEf4XlPxdbGr8DeEWjYmszZc2T4VT7npbKCHOrFEaZc52k6BwJ4CZJq5TdAQDK8X96tDE0K7azU5fowlz3f9ExjUo7FWKlF9k+00JPU5W0J3At8NGIeCI/rsj4IuLZiDiU7ErYRwC/U0QcVn5lyKNKyppb1TjnSuutEXE4cBxwlqS35UeW4btUhhiSy4HfBg4FNgMXFBpNjdqpEBvRrS0KsGVgV3F63pramx6vpF3JfigWR8Q3yhYfQEQ8DtwCvJlsF/fAte7yy38utjR+b+CxRsfWJsqaJ8Op9j0thVHmVqmMMOc6SaE5EhGb0vNW4DqyIrkM/6dHG0PDY4uILWmD4jfAFWSfVaEx1aKdCrGy3tpiOTBwtshsYFmu/bR0xslRwI7cbt+6S8dQXQk8EBEXlik+SftJmpCG9wCOITvO5hbgvVViG4j5vcB30haaDa+seTKcat/TwtWQW4WrIec6SWE5Imm8pL0GhoEZwH2U4P90DTHcCMyQNDF1Gc5IbXUz6Hi495B9VgMxnazsDPuDgGnAHZT1/1+RZwrU+0F29saPyY51+GQBy/8q2e7RX5P1PZ9JduzSzcBa4NvAPmlaAZelWFcD3Q2O7a1ku5LvBe5Jj+PLEB/wu8APUmz3Af8ntb+aLHn6gH8Hdk/tL02v+9L4Vxf93WulR9F5MoL4RpxHZXiMNrfK8BhtznXao6gcSZ//D9NjzcCym/1/ul6/ZcAH03epDzijATFdk5Z5L1lBNSk3/SdTTA8CxxX9tx3q4Svrm5mZmRWknbomzczMzFqKCzEzMzOzgrgQMzMzMyuICzEzMzOzgrgQMzMzMyuICzEzMzOzgrgQMzMzMyuICzEzMzOzgrgQMzMzMyuICzEzMzOzgrgQMzMzMyuICzEzMzOzgrgQMzMzMyuICzEzMzOzgrgQK4ikqyR9pknL+ktJWyTtlPSKJi1zqqSQtEszlmdmZtaKXIi1OEm/L+k7kp6UtEPSf0o6ODd+V+BCYEZE7An8k6TL8+MlPVWl7aimroyZmVmHcSHWwiS9GbgJWAbsDxwE/BD4b0mvTpN1AS8F1qTXtwJvy82mG/gp8AeD2gBWNSZyMzMzAxdiTSPpMEl3pz1XXyMrjpA0UdL1kn4uaXsaPiCNe5+kVYPm87eSlqWX/wxcHREXR8STEbEtIv4RuA04V9JrgQfTtI9L+g5ZIfZ6Sfum9j8AlgDjB7V9PyJ+LWl/Sdem+B6S9De5WF4iaZ6kn0h6TNJSSftUWf8/kbRe0hvG9kmamZm1DxdiTSBpN+A/gGuAfYB/B/4kjX4J8CXgQOBVwC+Az6Vxy4GDJL0+N7sPAFdLehnw+2legy0FjomIHwOHpLYJEfGOiNgAPMzze8DeBnwX+J9BbbdKegnwn2R72SYDRwMflXRsmu6vgROBPyTbI7cduKzC+p8BfBZ4Z0TcV+1zMjMz6zQuxJrjKGBX4F8j4tcR8XXgToCIeCwiro2IpyPiSeB8ssKGiHgG+BrwZwCSDgGmAteTFXQvATZXWN5mYN8K7QP+C3hbKrSOINuD9t1c21vSNL8H7BcRn46IX0XEOuAK4OQ0nw8Dn4yIjSnWc4H3DjpA/6PA3wM9EdE3kg/LzMysU7gQa479gU0REbm2hwEkvUzSFyQ9LOkJsq7DCZLGpekWAe+XJLK9YUtT0bMd+A0wqcLyJgGPDhHPwHFi04F1EfE08L1c2x7A7WR76faX9PjAA/gE2XFnpPHX5cY9ADybGw9ZEXZZRGwc8hMyMzPrQC7EmmMzMDkVUwNelZ7nAq8DjoyIl/P8gfQCiIjbgF+RdRu+n6x7k4h4Cvg+8L4KyzsJuHmIeG4F3gicQLYnDLKD+aektjsj4pfABuChiJiQe+wVEcen92wAjhs0/qURsSm3rBnAP0r6E8zMzOwFXIg1x/eBfuBv0qUh/pisSxBgL7Ljwh5PB7qfU+H9V5MdN/briPhern0eMFvS30jaKx34/xngzcCnqgWTugi3AB8hFWJpb93tqe3WNOkdwJOSPiZpD0njJL1B0u+l8Z8Hzpd0IICk/STNGrS4NcBM4DJJfzTkp2RmZtZhXIg1QUT8Cvhj4HRgG/CnwDfS6H8l6wp8lOxYrW9VmMU1wBuALw+a7/eAY9O8N5N1dx4GvDUi1g4T1q3AfsB/59q+C7wyjSMingXeBRwKPJRi/CKwd5r+YrITCm6S9GSK/8gK6//DNJ8rJB03TFxmZmYdQy88bMnKSNIewFbg8BEUWGZmZtYivEesNfwl2XFbLsLMzMzaiO8DWHKS1pMduH9isZGYmZlZvblr0szMzKwg7po0MzMzK0jLdk3uu+++MXXq1IrjnnrqKcaPH9/cgOrEsRdjqNhXrVr1aETs1+SQzMysA7RsITZ16lTuuuuuiuN6e3vp6elpbkB14tiLMVTskh5ubjRmZtYp3DVpZmZmVhAXYmZmZmYFcSFmZmZmVhAXYmZmZmYFqbkQkzRF0i2S7pe0RtJHUvs+klZKWpueJ6Z2SbpEUp+keyUdnpvX7DT9Wkmzx75aZmZmZuU3lrMm+4G5EXG3pL2AVZJWkt3Y+uaImC9pHjAP+BhwHDAtPY4ELgeOlLQPcA7QDUSaz/KI2F5rYKs37eD0eTeM+n3r559Q6yLNzMzMRq3mPWIRsTki7k7DTwIPAJOBWcCiNNkinr81zyzg6sjcBkyQNAk4FlgZEdtS8bUSmFlrXGZmZmatoi7XEZM0FTgMuB3oiojNadQjQFcangxsyL1tY2qr1l5pOXOAOQBdXV309vZWjKdrD5g7vX/U61Ftfs20c+fOUsSRt3rTjhFN17UHXLp42XOvp0/eu1Eh1V0ZP3czM2t/Yy7EJO0JXAt8NCKekPTcuIgISXW7mWVELAAWAHR3d0e1C3BeungZF6we/aqtP7Xy/JqpjBdFHWk379zp/S/43MvweY5UGT93MzNrf2M6a1LSrmRF2OKI+EZq3pK6HEnPW1P7JmBK7u0HpLZq7WZmZmZtbSxnTQq4EnggIi7MjVoODJz5OBtYlms/LZ09eRSwI3Vh3gjMkDQxnWE5I7WZmZmZtbWxdE2+BfgAsFrSPantE8B8YKmkM4GHgZPSuBXA8UAf8DRwBkBEbJN0HnBnmu7TEbFtDHG1jKkVuvzmTu8ftivQZ3eamZm1h5oLsYj4HqAqo4+uMH0AZ1WZ10JgYa2x1EulwmgkXBiZmZlZLepy1qS1hloLzWZzQWxmZp3CtzgyMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OCuBAzMzMzK4gLMTMzM7OC1FyISVooaauk+3Jt50raJOme9Dg+N+7jkvokPSjp2Fz7zNTWJ2le7atiZmZm1lrGskfsKmBmhfaLIuLQ9FgBIOlg4GTgkPSef5M0TtI44DLgOOBg4JQ0rZmZmVnb26XWN0bErZKmjnDyWcCSiHgGeEhSH3BEGtcXEesAJC1J095fa1xmZmZmraLmQmwIZ0s6DbgLmBsR24HJwG25aTamNoANg9qPrDZjSXOAOQBdXV309vZWnK5rD5g7vb/W+EetWhzDqRTjSGKv5/LqaXDszY6z1uUB7Ny5c0zvNzMzq0W9C7HLgfOASM8XAB+s18wjYgGwAKC7uzt6enoqTnfp4mVcsLoRNWZl60+tHMdwTp93w4va5k7vHzb2ei6vngbH3uw4a10eZEVcte+TmZlZo9S1WomILQPDkq4Ark8vNwFTcpMekNoYot3MzMysrdX18hWSJuVevgcYOKNyOXCypN0lHQRMA+4A7gSmSTpI0m5kB/Qvr2dMZmZmZmVV8x4xSV8FeoB9JW0EzgF6JB1K1jW5HvgQQESskbSU7CD8fuCsiHg2zeds4EZgHLAwItbUGpOZmZlZKxnLWZOnVGi+cojpzwfOr9C+AlhRaxxmZmZmrcpX1jczMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4K4EDMzMzMrSPNuyGjWYFPHcC/Nq2aOr2MkZmZmI+M9YmZmZmYFcSFmZmZmVhAXYmZmZmYFcSFmZmZmVhAXYmZmZmYFcSFmZmZmVhAXYmZmZmYFcSFmZmZmVhAXYmZmZmYFcSFmZmZmVhAXYmZmZmYFcSFmZmZmVhAXYmZmZmYFcSFmZmZmVhAXYmZmZmYFcSFmZmZmVhAXYmZmZmYFcSFmZmZmVpAxFWKSFkraKum+XNs+klZKWpueJ6Z2SbpEUp+keyUdnnvP7DT9WkmzxxKTmZmZWasY6x6xq4CZg9rmATdHxDTg5vQa4DhgWnrMAS6HrHADzgGOBI4Azhko3szMzMza2ZgKsYi4Fdg2qHkWsCgNLwJOzLVfHZnbgAmSJgHHAisjYltEbAdW8uLizszMzKzt7NKAeXZFxOY0/AjQlYYnAxty021MbdXaX0TSHLK9aXR1ddHb21s5gD1g7vT+GsMfvWpxDKdSjCOJvZ7Lq6fBsZc1zkp27txZc7xmZma1akQh9pyICElRx/ktABYAdHd3R09PT8XpLl28jAtWN3TVXmD9qZXjGM7p8254Udvc6f3Dxl7P5dXT4NjLGmclV80cT7Xvk5mZWaM04qzJLanLkfS8NbVvAqbkpjsgtVVrNzMzM2trjSjElgMDZz7OBpbl2k9LZ08eBexIXZg3AjMkTUwH6c9IbWZmZmZtbUz9d5K+CvQA+0raSHb243xgqaQzgYeBk9LkK4DjgT7gaeAMgIjYJuk84M403acjYvAJAGZmZmZtZ0yFWEScUmXU0RWmDeCsKvNZCCwcSyxmZmZmrcZX1jczMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4K4EDMzMzMriAsxMzMzs4I0rBCTtF7Sakn3SLorte0jaaWktel5YmqXpEsk9Um6V9LhjYrLzMzMrCwavUfs7RFxaER0p9fzgJsjYhpwc3oNcBwwLT3mAJc3OC4zMzOzwjW7a3IWsCgNLwJOzLVfHZnbgAmSJjU5NjMzM7OmUkQ0ZsbSQ8B2IIAvRMQCSY9HxIQ0XsD2iJgg6XpgfkR8L427GfhYRNw1aJ5zyPaY0dXV9aYlS5ZUXPbWbTvY8ouGrFZF0yfvXdP7Vm/a8aK2rj0YNvZ6Lq+eBsde1jgrOWjvcey5554Vx7397W9fldura2ZmVje7NHDeb42ITZJeCayU9KP8yIgISaOqAiNiAbAAoLu7O3p6eipOd+niZVywupGr9kLrT60cx3BOn3fDi9rmTu8fNvZ6Lq+eBsde1jgruWrmeKp9n8zMzBqlYV2TEbEpPW8FrgOOALYMdDmm561p8k3AlNzbD0htZmZmZm2rIYWYpPGS9hoYBmYA9wHLgdlpstnAsjS8HDgtnT15FLAjIjY3IjYzMzOzsmhU/10XcF12GBi7AF+JiG9JuhNYKulM4GHgpDT9CuB4oA94GjijQXGZmZmZlUZDCrGIWAe8sUL7Y8DRFdoDOKsRsZiZmZmVla+sb2ZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlYQF2JmZmZmBXEhZmZmZlaQ0hRikmZKelBSn6R5RcdjZmZm1milKMQkjQMuA44DDgZOkXRwsVGZmZmZNVYpCjHgCKAvItZFxK+AJcCsgmMyMzMzayhFRNExIOm9wMyI+PP0+gPAkRFx9qDp5gBz0svXAQ9WmeW+wKMNCrfRHHsxhor9wIjYr5nBmJlZZ9il6ABGIyIWAAuGm07SXRHR3YSQ6s6xF6OVYzczs9ZVlq7JTcCU3OsDUpuZmZlZ2ypLIXYnME3SQZJ2A04Glhcck5mZmVlDlaJrMiL6JZ0N3AiMAxZGxJoxzHLY7ssSc+zFaOXYzcysRZXiYH0zMzOzTlSWrkkzMzOzjuNCzMzMzKwgbVWIteptkiRNkXSLpPslrZH0kaJjGi1J4yT9QNL1RccyGpImSPq6pB9JekDSm4uOyczMOkfbHCOWbpP0Y+AYYCPZmZinRMT9hQY2ApImAZMi4m5JewGrgBNbIfYBkv4W6AZeHhHvKjqekZK0CPhuRHwxnbH7soh4vOCwzMysQ7TTHrGWvU1SRGyOiLvT8JPAA8DkYqMaOUkHACcAXyw6ltGQtDfwNuBKgIj4lYswMzNrpnYqxCYDG3KvN9JCxcwASVOBw4DbCw5lNP4V+AfgNwXHMVoHAT8HvpS6Vb8oaXzRQZmZWedop0Ks5UnaE7gW+GhEPFF0PCMh6V3A1ohYVXQsNdgFOBy4PCIOA54CWubYQjMza33tVIi19G2SJO1KVoQtjohvFB3PKLwF+CNJ68m6g98h6cvFhjRiG4GNETGw9/HrZIWZmZlZU7RTIdayt0mSJLLjlB6IiAuLjmc0IuLjEXFAREwl+8y/ExF/VnBYIxIRjwAbJL0uNR0NtMwJEmZm1vpKcYujemjAbZKa6S3AB4DVku5JbZ+IiBXFhdQx/hpYnIr3dcAZBcdjZmYdpG0uX2FmZmbWatqpa9LMzMyspbgQMzMzMyuICzEzMzOzgrgQMzMzMyuICzEzMzOzgrgQMzMzMyuICzEzMzOzgvx/uOvlNtdfgCUAAAAASUVORK5CYII=\n" 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", + "text/plain": [ + "
" + ] }, "metadata": { "needs_background": "light" @@ -133,4 +152,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} diff --git a/tests/setup_tests.py b/tests/setup_tests.py index 228e423..953aeb9 100644 --- a/tests/setup_tests.py +++ b/tests/setup_tests.py @@ -17,8 +17,8 @@ import pandas as pd from elasticsearch import helpers +from elasticsearch._sync.client import Elasticsearch -from eland.common import es_version from tests import ( ECOMMERCE_FILE_NAME, ECOMMERCE_INDEX_NAME, @@ -53,9 +53,9 @@ def _setup_data(es): # Delete index print("Deleting index:", index_name) - es.indices.delete(index=index_name, ignore=[400, 404]) + es.options(ignore_status=[400, 404]).indices.delete(index=index_name) print("Creating index:", index_name) - es.indices.create(index=index_name, body=mapping) + es.indices.create(index=index_name, **mapping) df = pd.read_json(json_file_name, lines=True) @@ -85,30 +85,28 @@ def _setup_data(es): print("Done", index_name) -def _update_max_compilations_limit(es, limit="10000/1m"): +def _update_max_compilations_limit(es: Elasticsearch, limit="10000/1m"): print("Updating script.max_compilations_rate to ", limit) - if es_version(es) < (7, 8): - body = {"transient": {"script.max_compilations_rate": limit}} - else: - body = { - "transient": { - "script.max_compilations_rate": "use-context", - "script.context.field.max_compilations_rate": limit, - } + es.cluster.put_settings( + transient={ + "script.max_compilations_rate": "use-context", + "script.context.field.max_compilations_rate": limit, } - es.cluster.put_settings(body=body) + ) -def _setup_test_mappings(es): +def _setup_test_mappings(es: Elasticsearch): # Create a complex mapping containing many Elasticsearch features - es.indices.delete(index=TEST_MAPPING1_INDEX_NAME, ignore=[400, 404]) - es.indices.create(index=TEST_MAPPING1_INDEX_NAME, body=TEST_MAPPING1) + es.options(ignore_status=[400, 404]).indices.delete(index=TEST_MAPPING1_INDEX_NAME) + es.indices.create(index=TEST_MAPPING1_INDEX_NAME, **TEST_MAPPING1) def _setup_test_nested(es): - es.indices.delete(index=TEST_NESTED_USER_GROUP_INDEX_NAME, ignore=[400, 404]) + es.options(ignore_status=[400, 404]).indices.delete( + index=TEST_NESTED_USER_GROUP_INDEX_NAME + ) es.indices.create( - index=TEST_NESTED_USER_GROUP_INDEX_NAME, body=TEST_NESTED_USER_GROUP_MAPPING + index=TEST_NESTED_USER_GROUP_INDEX_NAME, **TEST_NESTED_USER_GROUP_MAPPING ) helpers.bulk(es, TEST_NESTED_USER_GROUP_DOCS)